Search results for: recognition of business opportunities

Authors: B. Afsharipoor, E. Nazemi

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Artificial neural networks (ANNs) are powerful tool for prediction which can be trained based on a set of examples and thus, it would be useful for nonlinear image processing. The present paper reviews several paper regarding applications of ANN in image processing to shed the light on advantage and disadvantage of ANNs in this field. Different steps in the image processing chain including pre-processing, enhancement, segmentation, object recognition, image understanding and optimization by using ANN are summarized. Furthermore, results on using multi artificial neural networks are presented.

Keywords: neural networks, image processing, segmentation, object recognition, image understanding, optimization, MANN

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Authors: Bruno Pinto Vieira, Marco Antonio Calijorne Soares, Armando Sérgio de Aguiar Filho

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Nowadays, a massive amount of information has been produced by different data sources, including mobile devices and transactional systems. In this scenario, concerns arise on how to maintain or establish data quality, which is now treated as a product to be defined, measured, analyzed, and improved to meet consumers' needs, which is the one who uses these data in decision making and companies strategies. Information that reaches low levels of quality can lead to issues that can consume time and money, such as missed business opportunities, inadequate decisions, and bad risk management actions. The step of selecting, identifying, evaluating, and selecting data sources with significant quality according to the need has become a costly task for users since the sources do not provide information about their quality. Traditional data quality control methods are based on user experience or business rules limiting performance and slowing down the process with less than desirable accuracy. Using advanced machine learning algorithms, it is possible to take advantage of computational resources to overcome challenges and add value to companies and users. In this study, machine learning is applied to data quality analysis on different datasets, seeking to compare the performance of the techniques according to the dimensions of quality assessment. As a result, we could create a ranking of approaches used, besides a system that is able to carry out automatically, data quality assessment.

Keywords: machine learning, data quality, quality dimension, quality assessment

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Authors: Sankar Majumder

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Development of Rural Entrepreneurs requires a holistic approach involving social, economic, political, technical, and environmental and many other issues. It needs a thorough understanding of the economy and society. It's true that agricultural development, rural development and many other social and right based development programmes have resulted in the growth of income in the rural sector. The development of rural entrepreneurs is necessary to utilise these opportunities. Many programmes and policies in the spheres of organisational, financial, infrastructural and technical supports have been taken to promote rural industries. But if one looks at the growth and development of rural industrial units, especially the manufacturing units, the picture is not promising. This paper aims at analysing the possible causes and its solutions in terms of (1) Mind set of the society towards business as a livelihood; (2) Sufficiency and appropriateness of the existing organisational, financial, infrastructural and technical supports. The paper is based on secondary data on various aspects of rural enterprises and the author’s experiences in the course of his work as a practitioner in this field. Growth of units and employment in the rural industries shows that the entrepreneurs are more inclined towards trading units than towards manufacturing ventures. The growth of rural industries is constrained not by the insufficiency of the supply of finance but by the insufficient demand for finance. The task is to increase the supply of entrepreneurs by creating an entrepreneurial environment. Incubation for rural entrepreneurs is the need of the hour.

Keywords: business mind set, entrepreneurial environment, supply of finance, technical support

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Authors: Amna Khaliq

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In this research paper, a comparison study has been undertaken between Canada and Denmark to analyze the education system between the countries in entrepreneurship. Denmark, a land of high wages and high taxes, and Canada, a land of immigrants and opportunities, have seen a positive relationship in entrepreneurs' growth. They are both considered one of the top ten countries to start a business and to have government support globally. However, education is entirely free to Danish students, including university degrees, compared to Canadians, which can further hurdle for Canadian millennials to grow in the business world—the business experience more growth with educated entrepreneurs with international backgrounds in new immigrants. Denmark has seen a gradual increase in female entrepreneurs over the decade but is still lower than OECD countries. Compassionate management and work-life balance are prioritized in Denmark, unlike in Canada. Danish are early adopters of technology and have excellent infrastructure to support the technology industry, whereas Canada is still a service-oriented and manufacturer-based country. 2018 has been the highest number of opening businesses for Canada and Denmark. Some companies offer high wages, hiring bonuses, flexible working hours, wellness, and mental health benefits during Pandemic to keep the companies running and keep their workers' morale high. Pandemic has taught consumers new patterns to shop online. It is essential now to use technology and automation to increase productivity in businesses. Only those companies will survive that are applying this strategy. The Pandemic has ultimately changed entrepreneurs' and employees' behavior in the business world. Along with Ph.D. professors, entrepreneurs should be allowed to teach at learning intuitions. Millennials turn out to be the most entrepreneurial generation in both countries. Entrepreneurship education will only be beneficial when students create businesses and learn from real-life experiences. Managing physical, mental, emotional, and psychological health while dealing with high pressure in entrepreneurship are soft skills learned through practical work.

Keywords: entrepreneurship education, millennials, pandemic, Denmark, Canada

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Authors: Kristiana Haryanti, Theresia Dwi Hastuti, Agustine Eva Maria Soekesi

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Entrepreneurship is an important topic today that is widely discussed in the business world. The COVID-19 pandemic has devastated all businesses in the world, especially businesses at the micro-level. This study tries to prove the relationship between job satisfaction of micro-level business owners and family social support for their resilience. The respondents of this study amounted to 58 entrepreneurs. The results of this study indicate that there is a relationship between job satisfaction and social support with entrepreneurial resilience in continuing the family business.

Keywords: family business, family social support, job satisfaction, resilience

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Authors: Mirjana Dokmanovic

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In recent years, the disadvantaged and marginalised position of rural women in the Republic of Serbia has been recognised in a number of national strategies and policy papers. A number of measures have been adopted by the government aimed at economic empowerment of rural women and eliminating barriers to accessing decision making and economic and social opportunities. However, their implementation pace is still slow. The aim of the paper is to indicate the necessity of a comprehensive policy approach to eliminating discrimination against rural women that would include policy and financial commitments for enhancing agricultural and rural development as a whole, instead of taking fragmented measures targeting consequences instead of causes. The paper introduces main findings of the study of challenges, constraints, and opportunities of rural women in Serbia to enjoy their economic and social rights. The research methodology included the desk research and the qualitative analysis of the available data, statistics, policy papers, studies, and reports produced by the government, ministries and other governmental bodies, independent human rights bodies, and civil society organizations (CSOs). The findings of the study reveal that rural women are at great risk of poverty, particularly in remote areas, and when getting old or widowed. Young rural women working in agriculture are also in unfavorable position, as they do not have opportunities to enjoy their rights during pregnancy and maternity leave, childcare leave and leave due to the special care of a child. The study indicates that the main causes of their unfavorable position are related to the prevalent patriarchal surrounding and economic and social underdevelopment of rural areas in Serbia. Gender inequalities have been particularly present in accessing land and property rights, inheritance, education, social protection, healthcare, and decision making. Women living in the rural areas are exposed at high risk of discrimination in all spheres of public and private life that undermine their enjoyment of basic economic, social and cultural rights. The vulnerability of rural women to discrimination increases in cases of the intersectionality of other grounds of discrimination, such as disability, ethnicity, age, health condition and sexual discrimination. If they are victims of domestic violence, their experience lack of access to shelters and protection services. Despite the State’s recognition of the marginalized position of rural women, there is still a lack of a comprehensive policy approach to improving the economic and social position of rural women.

Keywords: agricultural and rural development, care economy, discrimination against women, economic and social rights, feminization of poverty, Republic of Serbia, rural women

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Authors: Aisultan Shoiynbek, Darkhan Kuanyshbay, Paulo Menezes, Akbayan Bekarystankyzy, Assylbek Mukhametzhanov, Temirlan Shoiynbek

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Speech emotion recognition (SER) has received increasing research interest in recent years. It is a common practice to utilize emotional speech collected under controlled conditions recorded by actors imitating and artificially producing emotions in front of a microphone. There are four issues related to that approach: emotions are not natural, meaning that machines are learning to recognize fake emotions; emotions are very limited in quantity and poor in variety of speaking; there is some language dependency in SER; consequently, each time researchers want to start work with SER, they need to find a good emotional database in their language. This paper proposes an approach to create an automatic tool for speech emotion extraction based on facial emotion recognition and describes the sequence of actions involved in the proposed approach. One of the first objectives in the sequence of actions is the speech detection issue. The paper provides a detailed description of the speech detection model based on a fully connected deep neural network for Kazakh and Russian. Despite the high results in speech detection for Kazakh and Russian, the described process is suitable for any language. To investigate the working capacity of the developed model, an analysis of speech detection and extraction from real tasks has been performed.

Keywords: deep neural networks, speech detection, speech emotion recognition, Mel-frequency cepstrum coefficients, collecting speech emotion corpus, collecting speech emotion dataset, Kazakh speech dataset

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Authors: Rubin Dan, Xingcai Wang, Ziyang Chen

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A chaotic time series noise reduction method based on the fusion of the local projection method, wavelet transform, and particle swarm algorithm (referred to as the LW-PSO method) is proposed to address the problem of false recognition due to noise in the recognition process of chaotic time series containing noise. The method first uses phase space reconstruction to recover the original dynamical system characteristics and removes the noise subspace by selecting the neighborhood radius; then it uses wavelet transform to remove D1-D3 high-frequency components to maximize the retention of signal information while least-squares optimization is performed by the particle swarm algorithm. The Lorenz system containing 30% Gaussian white noise is simulated and verified, and the phase space, SNR value, RMSE value, and K value of the 0-1 test method before and after noise reduction of the Schreiber method, local projection method, wavelet transform method, and LW-PSO method are compared and analyzed, which proves that the LW-PSO method has a better noise reduction effect compared with the other three common methods. The method is also applied to the classical system to evaluate the noise reduction effect of the four methods and the original system identification effect, which further verifies the superiority of the LW-PSO method. Finally, it is applied to the Chengdu rainfall chaotic sequence for research, and the results prove that the LW-PSO method can effectively reduce the noise and improve the chaos recognition rate.

Keywords: Schreiber noise reduction, wavelet transform, particle swarm optimization, 0-1 test method, chaotic sequence denoising

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Authors: Hamdi Amroun, Yacine Benziani, Mehdi Ammi

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In this paper, we want to determine whether the accurate prediction of nicotine concentration can be obtained by using a network of smart objects and an e-cigarette. The approach consists of, first, the recognition of factors influencing smoking cessation such as physical activity recognition and participant’s behaviors (using both smartphone and smartwatch), then the prediction of the configuration of the e-cigarette (in terms of nicotine concentration, power, and resistance of e-cigarette). The study uses a network of commonly connected objects; a smartwatch, a smartphone, and an e-cigarette transported by the participants during an uncontrolled experiment. The data obtained from sensors carried in the three devices were trained by a Long short-term memory algorithm (LSTM). Results show that our LSTM-based model allows predicting the configuration of the e-cigarette in terms of nicotine concentration, power, and resistance with a root mean square error percentage of 12.9%, 9.15%, and 11.84%, respectively. This study can help to better control consumption of nicotine and offer an intelligent configuration of the e-cigarette to users.

Keywords: Iot, activity recognition, automatic classification, unconstrained environment

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Authors: Reza Shakoori

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This paper presents a structural correction of Arabic and Persian strokes using manipulation of their chain codes in order to improve the rate and performance of Persian and Arabic handwritten word recognition systems. It collects pure and effective features to represent a character with one consolidated feature vector and reduces variations in order to decrease the number of training samples and increase the chance of successful classification. Our results also show that how the proposed approaches can simplify classification and consequently recognition by reducing variations and possible noises on the chain code by keeping orientation of characters and their backbone structures.

Keywords: Arabic, chain code normalization, OCR systems, image processing

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Authors: Jamshaid ul Rahman, Akhter Ali, Adnan Manzoor

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Learning-based systems have received increasing interest in recent years; recognition structures, including end-to-end speak recognition, are one of the hot topics in this area. A famous work on end-to-end speaker verification by using Angular Softmax Loss gained significant importance and is considered useful to directly trains a discriminative model instead of the traditional adopted i-vector approach. The margin-based strategy in angular softmax is beneficial to learn discriminative speaker embeddings where the random selection of margin values is a big issue in additive angular margin and multiplicative angular margin. As a better solution in this matter, we present an alternative approach by introducing a bit similar form of an additive parameter that was originally introduced for face recognition, and it has a capacity to adjust automatically with the corresponding margin values and is applicable to learn more discriminative features than the Softmax. Experiments are conducted on the part of Fisher dataset, where it observed that the additive parameter with angular softmax to train the front-end and probabilistic linear discriminant analysis (PLDA) in the back-end boosts the performance of the structure.

Keywords: additive parameter, angular softmax, speaker verification, PLDA

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Authors: Amritpal Slaich, Mark Elshaw

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This paper explores the development of a decision support framework for the introduction of business intelligence (BI) through operational research techniques for application by SMEs. Aligned with the goals of the new Midlands Enterprise Initiative of improving the skill levels of the Midlands workforce and addressing high levels of regional unemployment, we have developed a framework to increase the level of business intelligence used by SMEs to improve business decision-making. Many SMEs in the Midlands fail due to the lack of high quality decision making. Our framework outlines how universities can: engage with SMEs in the use of BI through operational research techniques; develop appropriate and easy to use Excel spreadsheet models; and make use of a process to allow SMEs to feedback their findings of the models. Future work will determine how well the framework performs in getting SMEs to apply BI to improve their decision-making performance.

Keywords: SMEs, decision support framework, business intelligence, operational research techniques

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Authors: Han Xue, Zhang Lanyue

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In order to seek more effective feature extraction technology, feature extraction method based on MFCC combined with vector hydrophone is exposed in the paper. The sound pressure signal and particle velocity signal of two kinds of ships are extracted by using MFCC and its evolution form, and the extracted features are fused by using fisher-ratio and correlated distance criterion. The features are then identified by BP neural network. The results showed that MFCC, First-Order Differential MFCC and Second-Order Differential MFCC features can be used as effective features for recognition of underwater targets, and the fusion feature can improve the recognition rate. Moreover, the results also showed that the recognition rate of the particle velocity signal is higher than that of the sound pressure signal, and it reflects the superiority of vector signal processing.

Keywords: vector information, MFCC, differential MFCC, fusion feature, BP neural network

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Authors: Zainab S. Abdullahi, Zakariyya H. Abdullahi, Sahnun Dahiru

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Student attendance in schools is a very important aspect in school management record. In recent years, security systems have become one of the most demanding systems in school. Every institute have its own method of taking attendance, many schools in Nigeria use the old fashion way of taking attendance. That is writing the students name and registration number in a paper and submitting it to the lecturer at the end of the lecture which is time-consuming and insecure, because some students can write for their friends without the lecturer’s knowledge. In this paper, we propose a system that takes attendance using face recognition. There are many automatic methods available for this purpose i.e. biometric attendance, but they all waste time, because the students have to follow a queue to put their thumbs on a scanner which is time-consuming. This attendance is recorded by using a camera attached in front of the class room and capturing the student images, detect the faces in the image and compare the detected faces with database and mark the attendance. The principle component analysis was used to recognize the faces detected with a high accuracy rate. The paper reviews the related work in the field of attendance system, then describe the system architecture, software algorithm and result.

Keywords: attendance system, face detection, face recognition, PCA

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Authors: Tamás Gyulavári, Erzsébet Malota

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The main purpose of our study was to investigate the effect of congruence between the perceived self and perceived culture personality on the evaluation of the examined countries as ideal business/tourist destinations. A measure of Culture Personality (CP) has been developed and implemented to assess the perception of French and Turkish culture. Results show that very similar personality structure of both cultures can be extracted along the dimensions of Competence, Interpersonal approach, Aura, Life approach and Rectitude. Regarding the congruence theory, we found that instead of the effect of similarity between the perceived culture personality and actual self, the more positively culture personality is perceived relative to the perceived self, the more positive attitude the individual has toward the country as business and tourist destination.

Keywords: culture personality, ideal business/tourist destination, personality, scale development

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Authors: Aishatu Ibrahim Birma, Aminu Tukur, Abdulkarim Abbass Gora

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Machine translation plays a vital role in the Field of Natural Language Processing (NLP), breaking down language barriers and enabling communication across diverse communities. In the context of Hausa, a widely spoken language in West Africa, mainly in Nigeria, effective translation systems are essential for enabling seamless communication and promoting cultural exchange. However, due to the unique linguistic characteristics of Hausa, accurate translation remains a challenging task. The research proposes an approach to improving the machine learning translation of Hausa by integrating Named Entity Recognition (NER) techniques. Named entities, such as person names, locations, organizations, and dates, are critical components of a language's structure and meaning. Incorporating NER into the translation process can enhance the quality and accuracy of translations by preserving the integrity of named entities and also maintaining consistency in translating entities (e.g., proper names), and addressing the cultural references specific to Hausa. The NER will be incorporated into Neural Machine Translation (NMT) for the Hausa to English Translation.

Keywords: machine translation, natural language processing (NLP), named entity recognition (NER), neural machine translation (NMT)

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Authors: Theresa Robinson Harris

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Small businesses are important to the United States economy, yet the majority struggle to remain relevant and close before their fifth year. This qualitative study explored small business failure by comparing the experiences of small-business owners to understand their involvement with leadership during the early stages of the business, and the impact of this on the firms’ ability to survive. Participants’ experiences from two groups were compared to glean an understanding of the leadership process, how leadership differs between the groups, and to see what themes or constructs emerged that could help to explain the high failure rate. Leadership was perceived to be important when envisioning a path for the future and when providing a platform for employees to succeed. Those who embraced leadership as a skillset were more likely to get through the challenges of the early developmental years while those ignoring the importance of leadership were more likely to close prematurely. These findings suggest a disconnect with regards to the understanding, role, and benefits of leadership in small organizations, particularly young organizations in the early stages of development.

Keywords: leadership, small business, entrepreneurship, success, failure

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Authors: Ässia Boukhatmi, Roger Nyffenegger

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The expansion of solar energy as a means to address the climate crisis is undisputed, but the increasing number of new photovoltaic (PV) modules being put on the market is simultaneously leading to increased challenges in terms of managing the growing waste stream. Many of the discarded modules are still fully functional but are often damaged by improper handling after disassembly or not properly tested to be considered for a second life. In addition, the collection rate for dismantled PV modules in several European countries is only a fraction of previous projections, partly due to the increased number of illegal exports. The underlying problem for those market imperfections is an insufficient data exchange between the different actors along the PV value chain, as well as the limited traceability of PV panels during their lifetime. As part of the Horizon 2020 project CIRCUSOL, an asset database prototype was developed to tackle the described problems. In an iterative process applying the design science research methodology, different business models, as well as the technical implementation of the database, were established and evaluated. To explore the requirements of different stakeholders for the development of the database, surveys and in-depth interviews were conducted with various representatives of the solar industry. The proposed database prototype maps the entire value chain of PV modules, beginning with the digital product passport, which provides information about materials and components contained in every module. Product-related information can then be expanded with performance data of existing installations. This information forms the basis for the application of data analysis methods to forecast the appropriate end-of-life strategy, as well as the circular economy potential of PV modules, already before they arrive at the recycling facility. The database prototype could already be enriched with data from different data sources along the value chain. From a business model perspective, the database offers opportunities both in the area of reuse as well as with regard to the certification of sustainable modules. Here, participating actors have the opportunity to differentiate their business and exploit new revenue streams. Future research can apply this approach to further industry and product sectors, validate the database prototype in a practical context, and can serve as a basis for standardization efforts to strengthen the circular economy.

Keywords: business model, circular economy, database, design science research, solar industry

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Authors: Aisha Noour, Nick Hubbard

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Learner motivation is considered an important premise for the Blended e-Learning (BL) method. BL is an effective learning method in multiple domains, which opens several opportunities for its participants to engage in the learning environment. This research explores the learners’ perspective of BL according to the Self-Determination Theory (SDT). It identifies the opportunities and challenges for using the BL in Logistics Education (LE) in Egyptian Higher Education (HE). SDT is approached from different perspectives within the relationship between Intrinsic Motivation (IM), Extrinsic Motivation (EM) and Amotivation (AM). A self-administered face-to-face questionnaire was used to collect data from learners who were geographically widely spread around three colleges of International Transport and Logistics (CILTs) at the Arab Academy for Science, Technology and Maritime Transport (AAST&MT) in Egypt. Six hundred and sixteen undergraduates responded to a questionnaire survey. Respondents were drawn from three branches in Greater Cairo, Alexandria, and Port Said. The data analysis used was SPSS 22 and AMOS 18.

Keywords: intrinsic motivation, extrinsic motivation, amotivation, blended e-learning, Self Determination Theory

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Authors: Reza Aghayan

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Numerically invariant signatures were introduced as a new paradigm of the invariant recognition for visual objects modulo a certain group of transformations. This paper shows that the current formulation suffers from noise and indeterminacy in the resulting joint group-signatures and applies the n-difference technique and the m-mean signature method to minimize their effects. In our experimental results of applying the proposed numerical scheme to generate joint group-invariant signatures, the sensitivity of some parameters such as regularity and mesh resolution used in the algorithm will also be examined. Finally, several interesting observations are made.

Keywords: Euclidean and affine geometry, differential invariant G-signature curves, numerically invariant joint G-signatures, object recognition, noise, indeterminacy

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Authors: Siyao Zhu, Yifang Xu

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After natural and man-made disasters, robots can bypass the danger, expedite the search, and acquire unprecedented situational awareness to design rescue plans. The hands-free requirement from the first responders excludes the use of tedious manual control and operation. In unknown, unstructured, and obstructed environments, natural-language-based supervision is not amenable for first responders to formulate, and is difficult for robots to understand. Brain-computer interface is a promising option to overcome the limitations. This study aims to test the feasibility of using electroencephalography (EEG) signals to decode human intentions and detect the level of consensus on robot-provided information. EEG signals were classified using machine-learning and deep-learning methods to discriminate search intentions and agreement perceptions. The results show that the average classification accuracy for intention recognition and consensus assessment is 67% and 72%, respectively, proving the potential of incorporating recognizable users’ bioelectrical responses into advanced robot-assisted systems for emergency response.

Keywords: consensus assessment, electroencephalogram, emergency response, human-robot collaboration, intention recognition, search and rescue

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Authors: Samanwita Paul

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Borrowing from Kabeer’s framework of empowerment, participation of women at Panchayat level politics (grassroots level of politics in India) has been conceptualized as a resource in the study and the impact of the same in influencing the policies at the grassroots as an agency. The study attempts to examine such intricacies in the dynamics of participation and policy formulation at the Panchayat level and to assess its overall impact in altering the recognition and redistribution of women. A conscious attempt has been made to go beyond formal politics and consider participants of the informal political processes as subjects of the study. Primary surveys were conducted for data collection in 4 Panchayat villages (from Jalpaiguri district in West Bengal) of which 2 wards from each were selected based on the nature of reservation of the panchayat seats. In-depth interviews with the Panchayat members and an approximate of 80 voters from each of the villages were conducted. This has been further analyzed with the aid of appropriate statistical tools and narratives. Preliminary findings show that women from vulnerable sections tend to participate more in the political process since it offers them a means of negotiating with their vulnerabilities however in case of its impact on policy formulation, the effect of women’s participation does to appear to be as profound.

Keywords: recognition, redistribution, political participation, women

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Authors: Jalal Karam

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The methods of Fourier, Laplace, and Wavelet Transforms provide transfer functions and relationships between the input and the output signals in linear time invariant systems. This paper shows the equivalence among these three methods and in each case presenting an application of the appropriate (Fourier, Laplace or Wavelet) to the convolution theorem. In addition, it is shown that the same holds for a direct integration method. The Biorthogonal wavelets Bior3.5 and Bior3.9 are examined and the zeros distribution of their polynomials associated filters are located. This paper also presents the significance of utilizing wavelets as effective tools in processing speech signals for common multimedia applications in general, and for recognition and compression in particular. Theoretically and practically, wavelets have proved to be effective and competitive. The practical use of the Continuous Wavelet Transform (CWT) in processing and analysis of speech is then presented along with explanations of how the human ear can be thought of as a natural wavelet transformer of speech. This generates a variety of approaches for applying the (CWT) to many paradigms analysing speech, sound and music. For perception, the flexibility of implementation of this transform allows the construction of numerous scales and we include two of them. Results for speech recognition and speech compression are then included.

Keywords: continuous wavelet transform, biorthogonal wavelets, speech perception, recognition and compression

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Authors: Franziska Mais, Till Gramberg

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The paper presents the development of a sustainable business model for a platform-based application tailored for sawing companies in small and medium-sized enterprises (SMEs). The focus is on the integration of sustainability principles into the design of the business model to ensure a technologically advanced, legally sound, and economically efficient solution. Easy2IoT is a research project that aims to enable companies in the prefabrication sheet metal and sheet metal processing industry to enter the Industrial Internet of Things (IIoT) with a low-threshold and cost-effective approach. The methodological approach of Easy2IoT includes an in-depth requirements analysis and customer interviews with stakeholders along the value chain. Based on these insights, actions, requirements, and potential solutions for smart services are derived. The structuring of the business ecosystem within the application plays a central role, whereby the roles of the partners, the management of the IT infrastructure and services, as well as the design of a sustainable operator model are considered. The business model is developed using the value proposition canvas, whereby a detailed analysis of the requirements for the business model is carried out, taking sustainability into account. This includes coordination with the business model patterns, according to Gassmann, and integration into a business model canvas for the Easy2IoT product. Potential obstacles and problems are identified and evaluated in order to formulate a comprehensive and sustainable business model. In addition, sustainable payment models and distribution channels are developed. In summary, the article offers a well-founded insight into the systematic development of a sustainable business model for platform-based applications in SME sawmills, with a particular focus on the synergy of ecological responsibility and economic efficiency.

Keywords: business model, sustainable business model, IIoT, IIoT-platform, industrie 4.0, big data

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Authors: Sharvari Parikh

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Climate change has always been looked upon as a threat. Increased use of fossil fuels, depletion of bio diversity, certain human activities, rising levels of Greenhouse Gas (GHG) emissions are the factors that have caused climate change. Climate change is creating new risks and aggravating the existing ones. The paper focuses on breaking the stereotypical perception of climate change and draws attention towards the constructive side of it. Researches around the world have concluded that climate change has provided us with many untapped opportunities. The next 15 years will be crucial, as it is in our hands whether we are able to grab these opportunities or just let the situation get worse. The world stands at a stage where we cannot think of making a choice between averting climate change and promoting growth and development. In fact, the solution to climate change itself has got economic opportunities. The data evidences from the paper show how we can create the opportunity to improve the lives of the world’s population at large through structural change which will promote environment friendly investments. Rising Investment in green energy and increased demand of climate friendly products has got ample of employment opportunities. Old technologies and machinery which are employed today lack efficiency and demand huge maintenance because of which we face high production cost. This can be drastically brought down by adaptation of Green technologies which are more accessible and affordable. Overall GDP of the world has been heavily affected in aggravating the problems arising out of increasing weather problems. Shifting to green economy can not only eliminate these costs but also build a sound economy. Accelerating the economy in direction of low-carbon future can lessen the burdens such as subsidies for fossil fuels, several public debts, unemployment, poverty, reduce healthcare expenses etc. It is clear that the world will be dragged into the ‘Darker phase’ if the current trends of fossil fuels and carbon are being consumed. Switching to Green economy is the only way in which we can lift the world from darker phase. Climate change has opened the gates for ‘Green and Clean economy’. It will also bring countries of the world together in achieving the common goal of Green Economy.

Keywords: climate change, economic opportunities, green economy, green technology

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Authors: Maria C. Almario, Pam Remer, Jeff Resse, Kathy Moran, Linda Theander Adam

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The identification of victims of human trafficking and consequential service provision is characterized by a significant disconnection between the estimated prevalence of this issue and the number of cases identified. This poses as tremendous problem for human rights advocates as it prevents data collection, information sharing, allocation of resources and opportunities for international dialogues. The current paper introduces the Inclusive Human Trafficking Checklist (IHTC) as a measurement methodology with theoretical underpinnings derived from dialectic theory. The presence of human trafficking in a person’s life is conceptualized as a dynamic and dialectic interaction between vulnerability and exploitation. The current papers explores the operationalization of exploitation and vulnerability, evaluates the metric qualities of the instrument, evaluates whether there are differences in assessment based on the participant’s profession, level of knowledge, and training, and assesses if users of the instrument perceive it as useful. A total of 201 participants were asked to rate three vignettes predetermined by experts to qualify as a either human trafficking case or not. The participants were placed in three conditions: business as usual, utilization of the IHTC with and without training. The results revealed a statistically significant level of agreement between the expert’s diagnostic and the application of the IHTC with an improvement of 40% on identification when compared with the business as usual condition While there was an improvement in identification in the group with training, the difference was found to have a small effect size. Participants who utilized the IHTC showed an increased ability to identify elements of identity-based vulnerabilities as well as elements of fraud, which according to the results, are distinctive variables in cases of human trafficking. In terms of the perceived utility, the results revealed higher mean scores for the groups utilizing the IHTC when compared to the business as usual condition. These findings suggest that the IHTC improves appropriate identification of cases and that it is perceived as a useful instrument. The application of the IHTC as a multidisciplinary instrumentation that can be utilized in legal and human services settings is discussed as a pivotal piece of helping victims restore their sense of dignity, and advocate for legal, physical and psychological reparations. It is noteworthy that this study was conducted with a sample in the United States and later re-tested in Colombia. The implications of the instrument for treatment conceptualization and intervention in human trafficking cases are discussed as opportunities for enhancement of victim well-being, restoration engagement and activism. With the idea that what is personal is also political, we believe that the careful observation and data collection in specific cases can inform new areas of human rights activism.

Keywords: exploitation, human trafficking, measurement, vulnerability, screening

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Authors: Talha Sareshwala

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The purpose of this research paper is to help entrepreneurs build an environment of trust, transparency and accountability necessary for fostering long term investment, financial stability and business integrity and to guide future Entrepreneurs into a promising future. The study presents a broader review on Corporate Governance, starting from its definition and antecedents. This is the most important aspect of ethical business. In fact, the 3 main pillars of corporate governance are: Transparency; Accountability; Security. The combination of these 3 pillars in running a company successfully and forming solid professional relationships among its stakeholders, which includes key managerial employees and, most important, the shareholders This paper is sharing an experience how an entrepreneur can act as a catalyst while ensuring them that ethics and transparency do pay in business when followed in true spirit and action.

Keywords: business, entrepreneur, ethics, governance, transparency.

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Authors: Evgeni Baratashvili, Nino Pailodze, Ana Bolkvadze, Giorgi Sulashvili

Abstract:

At the present stage in our country intensifies cooperation with different business cultures, actively developing the process of implementation of Georgia in the global business system that requires us to develop a specific concept, including in the field of management. With the entry of Georgia into the international community, exchange of experience will only intensify. It is clear that the achievement of goals such as the doubling of the National Product increase the competitiveness of Georgian enterprises can’t be recorded without foreign management experience. On the other hand, knowledge of the areas of comparative management can be used in the process of choosing the path of socio-economic development of Georgia.

Keywords: business cultures, comparative management, corporate culture, Georgian business, Anglo-Saxon model, Georgian civilization, anti-capitalist mentality, culture management

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Authors: Neziha Jaouedi, Noureddine Boujnah, Mohamed Salim Bouhlel

Abstract:

In the framework of human machine interaction systems enhancement, we focus throw this paper on human behavior analysis and action recognition. Human behavior is characterized by actions and reactions duality (movements, psychological modification, verbal and emotional expression). It’s worth noting that many information is hidden behind gesture, sudden motion points trajectories and speeds, many research works reconstructed an information retrieval issues. In our work we will focus on motion extraction, tracking and action recognition using wavelet network approaches. Our contribution uses an analysis of human subtraction by Gaussian Mixture Model (GMM) and body movement through trajectory models of motion constructed from kalman filter. These models allow to remove the noise using the extraction of the main motion features and constitute a stable base to identify the evolutions of human activity. Each modality is used to recognize a human action using wavelets of derived beta distributions approach. The proposed approach has been validated successfully on a subset of KTH and UCF sports database.

Keywords: feautures extraction, human action classifier, wavelet neural network, beta wavelet

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Authors: Hanee Ryu

Abstract:

The supporting policies for renewable energy have been designed to deploy renewable energy technology targeting domestic market. The government encourages market creation through obligations such as FIT or RPS on an energy supplier. With these policy measures, the securing vast market needs to induce technology development. Furthermore, it is crucial that ensuring developing market can make the environment nurture the renewable energy industry. Overseas expansion to countries being in demand is essential under immature domestic market. Extending its business abroad can make the domestic company get the knowledge through learning-by-doing. Besides, operation in the countries to be rich in renewable resources such as weather conditions helps to develop proven track record required for verifying technologies. This paper figures out the factor to hamper the global market entry and build up the strategies to overcome difficulties. Survey conducted renewable energy company having overseas experiences at least once. Based on the survey we check the obstacle against exporting home goods and services. As a result, securing funds is salient fact to proceed to business. It is difficult that only private bank or investment agencies participate in the project under uncertainty which renewable energy development project bears inherently. These uncertainties need public fund such as ODA to encourage private sectors to start a business. Furthermore, international organizations such as IRENA or multilateral development banks as WBG play a role to guarantee the investment including risk insurance against uncertainty. It can also manage excavation business cooperating with developing countries and supplement inadequate government funding involved. With survey results strategies to obtain the order, the international organization places are categorized according to the type of getting a contract. This paper suggests 3 types approaching to the international organization project (going through international competitive bidding, using ODA and project financing) and specifies the role of government to support the domestic firms with running out of funds. Under renewable energy industry environment where hard to being created as a spontaneous market, government policy approach needs to motivate the actors to get into the business. It is one of the good strategies that countries with the low demand of renewable energies participate in the project international agencies order in the developing countries having abundant resources. This provides crucial guidance for the formulation of renewable energy development policy and planning with consideration of business opportunities and funding.

Keywords: exporting strategies, multilateral development banks, promoting in developing countries, renewable energy technologies

Procedia PDF Downloads 513