Search results for: artificial agency

Authors: Sumaira Saleem, Sajjad Hussain, Asma Kashif Shahzad, Hina Shaheen

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This study aims at exploring the relationship between language and ideology and how such relationships are represented in the analysis of spoken texts, following Van Dijk’s Socio Cognitive Model (2002). In this study, it is tried to show that political Talk shows broadcast by Private TV channels are working apparatuses of ideology and store meanings which are not always obvious for readers. This analysis was about the situation created by Arslan Iftkhar, the son of ex-Chief Justice of Pakistan, Iftikhar Muhammad Chaudry and PTI Chief Imran Khan. Arslan Iftikhar submitted an application against Imran Khan that he is not able to become a member of parliament of Pakistan. In the application, he demanded the documents, which are submitted by Imran Khan at the time of Election to the Election Commission of Pakistan. Murad Ali from PTI also submitted an application against PM Nawaz Sharif to the Election Commission of Pakistan for providing the copies. It also suggests that these talk shows mystify the agency of processes by using various strategies. In other words, critical text analyses reveal how these choices enable speakers to manipulate the realizations of agency and power in the representation of action to produce particular meanings which are not always explicit for all readers.

Keywords: ECP, CDA, socio cognitive model, ideology, TV channels, power

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Authors: Faizan Hassan, Seema Kumari, V. P. Singh, Pradeep Das, Diwakar Singh Dinesh

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Phlebotomus argentipes is an established vector for Visceral Leishmaniasis in Indian subcontinent. Laboratory colonization of Sand flies is imperative in research on vectors, which requires a proper diet for their larvae and adult growth that ultimately affects their survival and fecundity. In most of the laboratories, adult Sand flies are reared on rabbit blood feeding/artificial blood feeding and their larvae on fine grinded rabbit faeces as a sole source of food. Rabbit faeces are unhygienic, difficult to handle, high mites infestation as well as owing to bad odour which creates menacing to human users ranging from respiratory problems to eye infection and most importantly it does not full fill all the nutrients required for proper growth and development. It is generally observed that the adult emergence is very low in comparison to egg hatched, which may be due to insufficient food nutrients provided to growing larvae. To check the role of food nutrients on larvae survival and adult emergence, a high protein rich artificial diet for sand fly larvae were used in this study. The composition of artificial diet to be tested includes fine grinded (9 gm each) Rice, Pea nuts & Soyabean balls. These three food ingredients are rich source of all essential amino acids along with carbohydrate and minerals which is essential for proper metabolism and growth. In this study artificial food was found significantly more effective for larval development and adult emergence than rabbit faeces alone (P value >0.05). The weight of individual larvae was also found higher in test pots than the control. This study suggest that protein plays an important role in insect larvae development and adding carbohydrate will also enhances the fecundity of insects larvae.

Keywords: artificial food, nutrients, Phlebotomus argentipes, sand fly

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Authors: Jessica Federman

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The current research offers a longitudinal and qualitative study design to examine how reciprocity improves relations between the homeless and various stakeholders within a community. The study examines a homeless shelter that sought to establish a facility within a community of Los Angeles, that was initially met with strong resistance and opposition from a variety of organizations due to deeply entrenched views about the negative impact of having homeless individuals within the community. The project tested an intervention model that targets the reduction of stigmatization of homeless individuals and promotes synergistic exchanges between conflicted organizational entities in communities. Years later, the data show that there has been a remarkable reversal in the perception of the agency by the very forces that initially prevented it from being established. This reversal was achieved by a few key strategic decisions. Community engagement was the first step toward changing people’s minds and demonstrating how the homeless shelter was helping to alleviate the problem of homelessness instead of contributing to it. Central to the non-profit’s success was the agency’s pioneering formulation of a treatment model known as, Reciprocal Community Engagement Model (RCEM). The model works by reintegrating the homeless back into society through relationship building within a network of programs that foster positive human connections. This approach aims to draw the homeless out of the debilitating isolation of their situation, reintegrate them through purposeful roles in the community while simultaneously providing a reciprocal benefit to the community at large. Through multilevel, simultaneous social interaction, RCEM has a direct impact not only on the homeless shelter’s clients but also for the community as well. The agency’s approach of RCEM led to their homeless clients getting out of the shelter and getting to work in the community directly alongside other community volunteers and for the benefit of other city and community organizations. This led to several opportunities for community members and residents to interact in meaningful ways. Through each successive exposure, the resident and community members’ distrust in one another was gradually eased and a mutually supportive relationship restored. In this process, the community member becomes the locus of change as much as the residents of the shelter. Measurements of community trust and resilience increased while negative perceptions of homeless people decreased.

Keywords: stigma, homelessness, reciprocity, identity

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Authors: Feng Wang, Shencheng Ni, Shuying Han, Shanhong You

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With the development of optical communication, optical performance monitoring (OPM) has received more and more attentions. Since optical signal-to-noise ratio (OSNR) is directly related to bit error rate (BER), it is one of the important parameters in optical networks. Recently, artificial neural network (ANN) has been greatly developed. ANN has strong learning and generalization ability. In this paper, a method of OSNR monitoring based on delay-tap sampling (DTS) and ANN has been proposed. DTS technique is used to extract the eigenvalues of the signal. Then, the eigenvalues are input into the ANN to realize the OSNR monitoring. The experiments of 10 Gb/s non-return-to-zero (NRZ) on–off keying (OOK), 20 Gb/s pulse amplitude modulation (PAM4) and 20 Gb/s return-to-zero (RZ) differential phase-shift keying (DPSK) systems are demonstrated for the OSNR monitoring based on the proposed method. The experimental results show that the range of OSNR monitoring is from 15 to 30 dB and the root-mean-square errors (RMSEs) for 10 Gb/s NRZ-OOK, 20 Gb/s PAM4 and 20 Gb/s RZ-DPSK systems are 0.36 dB, 0.45 dB and 0.48 dB respectively. The impact of chromatic dispersion (CD) on the accuracy of OSNR monitoring is also investigated in the three experimental systems mentioned above.

Keywords: artificial neural network (ANN), chromatic dispersion (CD), delay-tap sampling (DTS), optical signal-to-noise ratio (OSNR)

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Authors: Joshua Osondu

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This research explores how Artificial Intelligence (AI) can be utilized as a policy response to address teaching and learning (TL) issues in education in Ghana. The dual (AI and human) instructor model is used as a theoretical framework to examine how AI can be employed to improve teaching and learning processes and to equip learners with the necessary skills in the emerging AI society. A qualitative research design was employed to assess the impact of AI on various TL issues, such as teacher workloads, a lack of qualified educators, low academic performance, unequal access to education and educational resources, a lack of participation in learning, and poor access and participation based on gender, place of origin, and disability. The study concludes that AI can be an effective policy response to TL issues in Ghana, as it has the potential to increase students’ participation in learning, increase access to quality education, reduce teacher workloads, and provide more personalized instruction. The findings of this study are significant for filling in the gaps in AI research in Ghana and other developing countries and for motivating the government and educational institutions to implement AI in TL, as this would ensure quality, access, and participation in education and help Ghana industrialize.

Keywords: artificial intelligence, teacher, learner, students, policy response

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Authors: Gabriela Wasileski, Debra L. Stanley

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Maryland efforts to decrease the juvenile criminality and recidivism shifts towards evidence based sentencing. While in theory the evidence based sentencing has an impact on the reduction of juvenile delinquency and drug abuse; the assessment of juveniles’ risk and needs usually lacks crucial information about juvenile’s prior victimization. The Maryland Comprehensive Assessment and Service Planning (MCASP) Initiative is the primary tool for developing and delivering a treatment service plan for juveniles at risk. Even though it consists of evidence-based screening and assessment instruments very little is currently known regarding the effectiveness and the impact of the assessment in general. In keeping with Maryland’s priority to develop successful evidence-based recidivism reduction programs, this study examined results of assessments based on MCASP using a representative sample of the juveniles at risk and their assessment results. Specifically, it examined: (1) the results of the assessments in an electronic database (2) areas of need that are more frequent among delinquent youth in a system/agency, (3) the overall progress of youth in an agency’s care (4) the impact of child victimization and trauma experiences reported in the assessment. The project will identify challenges regarding the use of MCASP in Maryland, and will provide a knowledge base to support future research and practices.

Keywords: Juvenile Justice, assessment of risk and need, victimization and crime, recidivism

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Authors: Boris M. Fedorov, Liudmila P. Goncharenko, Sergey A. Sybachin, Natalia A. Mamedova, Ekaterina V. Makarenkova, Saule Rakhimova

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In modern economic conditions, the question of the possibility of correct recognition of a signature on digital documents in order to verify the expression of will or confirm a certain operation is relevant. The additional complexity of processing lies in the dynamic variability of the signature for each individual, as well as in the way information is processed because the signature refers to biometric data. The article discusses the issues of using artificial intelligence models in order to improve the quality of signature confirmation in document recognition. The analysis of several possible options for using the model is carried out. The results of the study are given, in which it is possible to correctly determine the authenticity of the signature on small samples.

Keywords: signature recognition, biometric data, artificial intelligence, neural networks

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Authors: Abbas Ahmadi, Bijan Raie, Mohammad Reza Neyshabouri, Mohammad Ali Ghorbani, Farrokh Asadzadeh

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In recent years, vast areas of Urmia Lake in Dasht-e-Tabriz has dried up leading to saline sediments exposure on the surface lake coastal areas being highly susceptible to wind erosion. This study was conducted to investigate wind erosion and its relevance to soil physicochemical properties and also modeling of wind erodibility (WE) using artificial intelligence techniques. For this purpose, 96 soil samples were collected from 0-5 cm depth in 414000 hectares using stratified random sampling method. To measure the WE, all samples (<8 mm) were exposed to 5 different wind velocities (9.5, 11, 12.5, 14.1 and 15 m s-1 at the height of 20 cm) in wind tunnel and its relationship with soil physicochemical properties was evaluated. According to the results, WE varied within the range of 76.69-9.98 (g m-2 min-1)/(m s-1) with a mean of 10.21 and coefficient of variation of 94.5% showing a relatively high variation in the studied area. WE was significantly (P<0.01) affected by soil physical properties, including mean weight diameter, erodible fraction (secondary particles smaller than 0.85 mm) and percentage of the secondary particle size classes 2-4.75, 1.7-2 and 0.1-0.25 mm. Results showed that the mean weight diameter, erodible fraction and percentage of size class 0.1-0.25 mm demonstrated stronger relationship with WE (coefficients of determination were 0.69, 0.67 and 0.68, respectively). This study also compared efficiency of multiple linear regression (MLR), gene expression programming (GEP), artificial neural network (MLP), artificial neural network based on genetic algorithm (MLP-GA) and artificial neural network based on whale optimization algorithm (MLP-WOA) in predicting of soil wind erodibility in Dasht-e-Tabriz. Among 32 measured soil variable, percentages of fine sand, size classes of 1.7-2.0 and 0.1-0.25 mm (secondary particles) and organic carbon were selected as the model inputs by step-wise regression. Findings showed MLP-WOA as the most powerful artificial intelligence techniques (R2=0.87, NSE=0.87, ME=0.11 and RMSE=2.9) to predict soil wind erodibility in the study area; followed by MLP-GA, MLP, GEP and MLR and the difference between these methods were significant according to the MGN test. Based on the above finding MLP-WOA may be used as a promising method to predict soil wind erodibility in the study area.

Keywords: wind erosion, erodible fraction, gene expression programming, artificial neural network

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Authors: Ge Zheng, Jun Peng

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Unbalanced power output from individual cells of Proton Exchange Membrane Fuel Cell (PEMFC) has direct effects on PEMFC stack performance, in particular under transient operation. In the paper, a multi-layer ANN (Artificial Neural Network) model Radial Basis Functions (RBF) has been developed for predicting cells' output profiles by applying gas supply parameters, cooling conditions, temperature measurement of individual cells, etc. The feed-forward ANN model was validated with experimental data. Influence of relevant parameters of RBF on the network accuracy was investigated. After adequate model training, the modelling results show good correspondence between actual measurements and reconstructed output profiles. Finally, after the model was used to optimize the stack output performance under steady-state and transient operating conditions, it suggested that the developed ANN control model can help PEMFC stack to have obvious improvement on power output under fast acceleration process.

Keywords: proton exchange membrane fuel cell, PEMFC, artificial neural network, ANN, cell output profile, transient

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Authors: Sirilak Areerachakul

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Water quality has initiated serious management efforts in many countries. Artificial Neural Network (ANN) models are developed as forecasting tools in predicting water quality trend based on historical data. This study endeavors to automatically classify water quality. The water quality classes are evaluated using 6 factor indices. These factors are pH value (pH), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen (NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (T-Coliform). The methodology involves applying data mining techniques using multilayer perceptron (MLP) neural network models. The data consisted of 11 sites of Saen Saep canal in Bangkok, Thailand. The data is obtained from the Department of Drainage and Sewerage Bangkok Metropolitan Administration during 2007-2011. The results of multilayer perceptron neural network exhibit a high accuracy multilayer perception rate at 94.23% in classifying the water quality of Saen Saep canal in Bangkok. Subsequently, this encouraging result could be combined with GIS data improves the classification accuracy significantly.

Keywords: artificial neural network, geographic information system, water quality, computer science

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Authors: Milda Istiqomah

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Deradicalization program has long been the subject of investigation. There is a steadily growing interest in examining the results on how Islamist terrorists agree to abandon violence and leave radicalism; however, it is argued that de-radicalization program on terrorism in many countries is still questionable for its effectiveness. This article aims to provide an overview of the deradicalization program specifically related to the victim-centred approach conducted by the Indonesian government and investigates critical issues surrounding the analysis of their effectiveness and outcomes. This research employs several case studies of a victim-centred approach conducted by the Indonesian Witness and Victim Protection Agency as well as the Indonesian Counter-terrorism Agency. This paper argues that the victim-centred approach to de-radicalize former terrorist prisoners faces several implemental challenges; however, the initiative may offer promise for future successful de-radicalization program. Furthermore, until more data surrounding the efficacy of this initiative available, the victim-centred approach may also constitute a significant and essential component of disengagement, de-radicalisation, and reintegration of terrorist prisoners. In conclusion, this paper suggests that further empirical research concerning prevention policies and disengagement interventions related to victim-centred approach need to be explored to give more inputs to the Indonesian government to achieve the effectiveness of de-radicalization program.

Keywords: terrorism, victim-centred approach, de-radicalization, Islamist extremism

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Authors: Shahd Alshammari

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This paper considers one of Margaret Atwood’s short stories ‘Lusus Naturae'. Through a critical lens that makes use of Julia Kristeva’s work on Powers of Horror and abjection, this paper suggests that the monstrous girl is the disabled woman, the abject in society. The monster is used as a metaphor for the unknown, the misunderstood, and the ‘different’ woman. Culturally Relevant Teaching (CRT) is a pedagogy that calls for making course material accessible and relevant to students. Through the study of literary texts, we are able to help create agency inside and outside the classroom. Stories are a necessary part of establishing connections across borders and boundaries. Stories are meant to raise awareness both inside and outside the classroom. The discussion is equally important, and the text is meant to facilitate relevant questions that the students need to consider when it comes to identity. Questions to consider are: what does it mean to be a ‘girl’ today, and what implications and consequences are at hand when you fail to perform this gendered identity? Gender is sometimes a fatal bond in the Middle East, and even more so, is the disability. In the case of our unnamed protagonist, she undergoes a process of un-becoming, a non-linear process of growing up. In a sense, it is a counter-Bildungsroman. The reading of this text emphasizes that a non-linear narrative is sometimes necessary for the female protagonist’s self-awareness and development. Discussion in class facilitates this sense of agency and questioning of gender and disability.

Keywords: disability, gender, literature, pedagogy

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Authors: Angelis P. Barlampas

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Target: The (SARS-CoV-2) is still a threat. AI software could be useful, categorizing the disease into different severities and indicate the extent of the lesions. Materials and methods: AI is a new revolutionary technique, which uses powered computerized systems, to do what a human being does more rapidly, more easily, as accurate and diagnostically safe as the original medical report and, in certain circumstances, even better, saving time and helping the health system to overcome problems, such as work overload and human fatigue. Results: It will be given an effort to describe to the inexperienced reader (see figures), as simple as possible, how an artificial intelligence system diagnoses computed tomography pictures. First, the computerized machine learns the physiologic motives of lung parenchyma by being feeded with normal structured images of the lung tissue. Having being used to recognizing normal structures, it can then easily indentify the pathologic ones, as their images do not fit to known normal picture motives. It is the same way as when someone spends his free time in reading magazines with quizzes, such as <> and <>. General conclusion: The AI mimics the physiological processes of the human mind, but it does that more efficiently and rapidly and provides results in a few seconds, whereas an experienced radiologist needs many days to do that, or even worse, he is unable to accomplish such a huge task.

Keywords: covid-19, artificial intelligence, automated imaging, CT, chest imaging

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Authors: Agada N. Ihuoma, Nagata Yasunori

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Artificial intelligence (AI) techniques play a crucial role in predicting the expected energy outcome and its performance, analysis, modeling, and control of renewable energy. Renewable energy is becoming more popular for economic and environmental reasons. In the face of global energy consumption and increased depletion of most fossil fuels, the world is faced with the challenges of meeting the ever-increasing energy demands. Therefore, incorporating artificial intelligence to predict solar radiation outcomes from the intermittent sunlight is crucial to enable a balance between supply and demand of energy on loads, predict the performance and outcome of solar energy, enhance production planning and energy management, and ensure proper sizing of parameters when generating clean energy. However, one of the major problems of forecasting is the algorithms used to control, model, and predict performances of the energy systems, which are complicated and involves large computer power, differential equations, and time series. Also, having unreliable data (poor quality) for solar radiation over a geographical location as well as insufficient long series can be a bottleneck to actualization. To overcome these problems, this study employs the anaconda Navigator (Jupyter Notebook) for machine learning which can combine larger amounts of data with fast, iterative processing and intelligent algorithms allowing the software to learn automatically from patterns or features to predict the performance and outcome of Solar Energy which in turns enables the balance of supply and demand on loads as well as enhance production planning and energy management.

Keywords: artificial Intelligence, backward elimination, linear regression, solar energy

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Authors: Jui-Ting Hsu, Heng-Li Huang, Ming-Tzu Tsai, Kuo-Chih Su, Lih-Jyh Fuh

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The objectives of this study were to measure the initial stability of the dental implant (ISQ and PTV) in the artificial foam bone block with three different quality levels. In addition, the 3D bone to implant contact percentage (BIC%) was measured based on the micro-computed tomography images. Furthermore, the relation between the initial stability of dental implant (ISQ and PTV) and BIC% were calculated. The experimental results indicated that enhanced the material property of the artificial foam bone increased the initial stability of the dental implant. The Pearson’s correlation coefficient between the BIC% and the two approaches (ISQ and PTV) were 0.652 and 0.745.

Keywords: dental implant, implant stability quotient, peak insertion torque, bone-implant contact, micro-computed tomography

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Authors: Kasun Gayashan Samarawickrama

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Bone diseases, defects, and fractions are commonly seen in modern life. Since bone is regenerating dynamic living tissue, it will undergo healing process naturally, it cannot recover from major bone injuries, diseases and defects. In order to overcome them, bone grafting technique was introduced. Gold standard was the best method for bone grafting for the past decades. Due to limitations of gold standard, alternative methods have been implemented. Apart from them artificial bone substitutes and bone tissue engineering have become the emerging methods with technology for bone grafting. Many bone diseases and defects will be healed permanently with these promising techniques in future.

Keywords: bone grafting, gold standard, bone substitutes, bone tissue engineering

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Authors: Begum Koru, Isik Aybay, Demet Celik Ulusoy

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The discipline of law is quite complex and has its own terminology. Apart from written legal rules, there is also living law, which refers to legal practice. Basic legal rules aim at the happiness of individuals in social life and have different characteristics in different branches such as public or private law. On the other hand, law is a national phenomenon. The law of one nation and the legal system applied on the territory of another nation may be completely different. People who are experts in a particular field of law in one country may have insufficient expertise in the law of another country. Today, in addition to the local nature of law, international and even supranational law rules are applied in order to protect basic human values and ensure the protection of human rights around the world. Systems that offer algorithmic solutions to legal problems using artificial intelligence (AI) tools will perhaps serve to produce very meaningful results in terms of human rights. However, algorithms to be used should not be developed by only computer experts, but also need the contribution of people who are familiar with law, values, judicial decisions, and even the social and political culture of the society to which it will provide solutions. Otherwise, even if the algorithm works perfectly, it may not be compatible with the values of the society in which it is applied. The latest developments involving the use of AI techniques in legal systems indicate that artificial law will emerge as a new field in the discipline of law. More AI systems are already being applied in the field of law, with examples such as predicting judicial decisions, text summarization, decision support systems, and classification of documents. Algorithms for legal systems employing AI tools, especially in the field of prediction of judicial decisions and decision support systems, have the capacity to create automatic decisions instead of judges. When the judge is removed from this equation, artificial intelligence-made law created by an intelligent algorithm on its own emerges, whether the domain is national or international law. In this work, the aim is to make a general analysis of this new topic. Such an analysis needs both a literature survey and a perspective from computer experts' and lawyers' point of view. In some societies, the use of prediction or decision support systems may be useful to integrate international human rights safeguards. In this case, artificial law can serve to produce more comprehensive and human rights-protective results than written or living law. In non-democratic countries, it may even be thought that direct decisions and artificial intelligence-made law would be more protective instead of a decision "support" system. Since the values of law are directed towards "human happiness or well-being", it requires that the AI algorithms should always be capable of serving this purpose and based on the rule of law, the principle of justice and equality, and the protection of human rights.

Keywords: AI and law, artificial law, protection of human rights, AI tools for legal systems

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Authors: Zouhour Neji Ben Salem

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Microscopic evacuation focuses on the evacuee behavior and way of search of safety place in an egress situation. In recent years, several models handled microscopic evacuation problem. Among them, we have proposed Artificial Neural Network (ANN) as an alternative to mathematical models that can deal with such problem. In this paper, we present two ANN models: SOM map and Hopfield Network used to predict the evacuee behavior in a disaster situation. These models are tested in a real case, the second floor of Tunisian children hospital evacuation in case of fire. The two models are studied and compared in order to evaluate their performance.

Keywords: artificial neural networks, self-organization map, hopfield network, microscopic evacuation, fire building evacuation

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Authors: Abilio Pires Zacarias

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Analyzing company boards of directors is a relevant and timely topic. As the representative of shareholders, the board is the most senior management body of this type of company. Therefore, ascertaining the best kind of candidates to nominate, namely the most appropriate characteristics for leading the board to achieve better levels of performance, is certainly of great interest. The companies selected for this study were the 1,000 largest non-financial companies and the 100 largest financial companies in Portugal according to the Instituto Nacional de Estatística for 2010. The information stemmed from a questionnaire addressed to the person in charge of daily company management and then processed through STATA 17 with the multivariate analysis of variables - MANOVA. The study may correspondingly report that the vast majority of boards in the sample operate a dual leadership structure. By in terms of its prevalence, unitary leadership represents only a minority. Agency theory and stewardship theory postulate different characteristics for the ideal chairman but neither receive confirmation from our results. On the other hand, our findings do validate the behavioral theory of firms (BToF), concluding that experience is associated with organizational performance. This study is also relevant due to its analysis of companies not listed on the financial markets not only because of their weighting in the economy but also because they remain only very poorly studied in this field and thus also correspondingly contributing to deepening the literature.

Keywords: agency theory, behavioral theory of the firm, board of directors, corporate governance, stewardship theory

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Authors: Giuseppina Settanni, Antonio Panarese, Raffaele Vaira, Maurizio Galiano

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Nowdays, artificial intelligence is used successfully in academia and industry for its ability to learn from a large amount of data. In particular, in recent years the use of machine learning algorithms in the field of e-commerce has spread worldwide. In this research study, a prototype software platform was designed and implemented in order to suggest to users the most suitable products for their needs. The platform includes a chatbot and a recommender system based on artificial intelligence algorithms that provide suggestions and decision support to the customer. The recommendation systems perform the important function of automatically filtering and personalizing information, thus allowing to manage with the IT overload to which the user is exposed on a daily basis. Recently, international research has experimented with the use of machine learning technologies with the aim to increase the potential of traditional recommendation systems. Specifically, support vector machine algorithms have been implemented combined with natural language processing techniques that allow the user to interact with the system, express their requests and receive suggestions. The interested user can access the web platform on the internet using a computer, tablet or mobile phone, register, provide the necessary information and view the products that the system deems them most appropriate. The platform also integrates a dashboard that allows the use of the various functions, which the platform is equipped with, in an intuitive and simple way. Artificial intelligence algorithms have been implemented and trained on historical data collected from user browsing. Finally, the testing phase allowed to validate the implemented model, which will be further tested by letting customers use it.

Keywords: machine learning, recommender system, software platform, support vector machine

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Authors: Belkacem Selma, Boumediene Selma, Tourkia Guerzou, Abbes Labdelli

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Natural disasters have occurred and will continue to cause human and material damage. Therefore, the idea of "preventing" natural disasters will never be possible. However, their prediction is possible with the advancement of technology. Even if natural disasters are effectively inevitable, their consequences may be partly controlled. The rapid growth and progress of artificial intelligence (AI) had a major impact on the prediction of natural disasters and risk assessment which are necessary for effective disaster reduction. The Earthquakes prediction to prevent the loss of human lives and even property damage is an important factor; that is why it is crucial to develop techniques for predicting this natural disaster. This present study aims to analyze the ability of artificial neural networks (ANNs) to predict earthquakes that occur in a given area. The used data describe the problem of high energy (higher than 10^4J) seismic bumps forecasting in a coal mine using two long walls as an example. For this purpose, seismic bumps data obtained from mines has been analyzed. The results obtained show that the ANN with high accuracy was able to predict earthquake parameters; the classification accuracy through neural networks is more than 94%, and that the models developed are efficient and robust and depend only weakly on the initial database.

Keywords: earthquake prediction, ANN, seismic bumps

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Authors: Sayor Ajfar Aaron, Sajjat Hossain Abir, Ashif Newaz, Mushfiqur Rahman

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Investigating the convergence of blockchain technology and artificial intelligence, this paper examines their synergistic effects on data integrity and decision-making within smart contracts. By implementing AI-driven analytics on blockchain-based platforms, the research identifies improvements in automated contract enforcement and decision accuracy. The paper presents a framework that leverages AI to enhance transparency and trust while blockchain ensures immutable record-keeping, culminating in significantly optimized operational efficiencies in various industries.

Keywords: artificial intelligence, blockchain, data integrity, smart contracts

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Authors: Juan Sebastian Huertas Piedrahita, Jaime Arturo Lopez Duque, Eduardo Luis Perez Londoño, Julián S. Rodríguez

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The process of generating computer vision is called artificial vision. The artificial vision is a branch of artificial intelligence that allows the obtaining, processing, and analysis of any type of information especially the ones obtained through digital images. Actually the artificial vision is used in manufacturing areas for quality control and production, as these processes can be realized through counting algorithms, positioning, and recognition of objects that can be measured by a single camera (or more). On the other hand, the companies use assembly lines formed by conveyor systems with actuators on them for moving pieces from one location to another in their production. These devices must be previously programmed for their good performance and must have a programmed logic routine. Nowadays the production is the main target of every industry, quality, and the fast elaboration of the different stages and processes in the chain of production of any product or service being offered. The principal base of this project is to program a computer that recognizes geometric figures (circle, square, and triangle) through a camera, each one with a different color and link it with a group of conveyor systems to organize the mentioned figures in cubicles, which differ from one another also by having different colors. This project bases on artificial vision, therefore the methodology needed to develop this project must be strict, this one is detailed below: 1. Methodology: 1.1 The software used in this project is QT Creator which is linked with Open CV libraries. Together, these tools perform to realize the respective program to identify colors and forms directly from the camera to the computer. 1.2 Imagery acquisition: To start using the libraries of Open CV is necessary to acquire images, which can be captured by a computer’s web camera or a different specialized camera. 1.3 The recognition of RGB colors is realized by code, crossing the matrices of the captured images and comparing pixels, identifying the primary colors which are red, green, and blue. 1.4 To detect forms it is necessary to realize the segmentation of the images, so the first step is converting the image from RGB to grayscale, to work with the dark tones of the image, then the image is binarized which means having the figure of the image in a white tone with a black background. Finally, we find the contours of the figure in the image to detect the quantity of edges to identify which figure it is. 1.5 After the color and figure have been identified, the program links with the conveyor systems, which through the actuators will classify the figures in their respective cubicles. Conclusions: The Open CV library is a useful tool for projects in which an interface between a computer and the environment is required since the camera obtains external characteristics and realizes any process. With the program for this project any type of assembly line can be optimized because images from the environment can be obtained and the process would be more accurate.

Keywords: artificial intelligence, artificial vision, binarized, grayscale, images, RGB

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Authors: Ana Luiza Aranha

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This paper contributes to the discussions that consider corruption as a challenge to the establishment of more democratically inclusive societies in Latin America. The paper advocates an intrinsic connection between democratic principles and corruption control – it is only possible to achieve just forms of democratic life if accountability institutions are able to control corruption, and therefore control the political exclusions that it brings. Departing from a non-trivial approach to corruption, and recognizing a gap in democratic theory when thinking about this phenomenon, corruption is understood as the breakdown of the democratic inclusive rule, whereby political decisions are made (and actions were taken) in spite of those potentially affected by them. Based on this idea, this paper proposes a new way of measuring corruption, moving away from usual aggregate measures – such as the Corruption Perception Index – and case studies of corruption scandals. The main argument sustains that corruption is intrinsically connected with the ability to be accountable and to provide public justification for the political conduct. The point advocated is that corruption involves a dimension of political exclusion. It generates a private benefit which is, from a democratic point of view, illegitimate, since it benefits some at the expense of the decisions made by the political community. Corruption is then a form of exclusion based on deception and opacity - for corruption, there is no plausible justification. Empirically, the paper uses the audit reports produced by the Brazilian anti-corruption agency (the CGU - Office of the Comptroller General) in its Inspections From Public Lotteries Program to exemplify how we can use this definition to separate corruption cases from mismanagement irregularities. On one side, there is poor management and inefficiencies, and, on the other, corruption, defined by the implausibility of public justifications – because the public officials would have to publicize illegitimate privileges and undue advantages. CGU reports provide the justifications given by the public officials for the irregularities found and also the acceptance or not by the control agency of these justifications. The analysis of this dialogue – between public officials and control agents – makes it possible to divide the irregularities on those that can be publicly justified versus those that cannot. In order to hold public officials accountable for their actions, making them responsible for the exclusions that they may cause (such as corruption), the accountability institutions fulfil an important role in reinforcing and empowering democracy and its basic inclusive condition.

Keywords: accountability, brazil, corruption, democracy

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Authors: Siti N. I. Mat Kamal, Othman Ibrahim, Mehrbakhsh Nilashi, Jafalizan M. Jali

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The use of digital forensics (DF) is nowadays essential for law enforcement agencies to identify analysis and interpret the digital information derived from digital sources. In Malaysia, the engagement of Malaysian Law Enforcement Agencies (MLEA) with this new technology is not evenly distributed. To investigate the factors influencing the adoption of DF in Malaysia law enforcement agencies’ operational environment, this study proposed the initial theoretical framework based on the integration of technology organization environment (TOE), institutional theory, and human organization technology (HOT) fit model. A questionnaire survey was conducted on selected law enforcement agencies in Malaysia to verify the validity of the initial integrated framework. Relative advantage, compatibility, coercive pressure, normative pressure, vendor support and perceived technical competence of technical staff were found as the influential factors on digital forensics adoption. In addition to the only moderator of this study (agency size), any significant moderating effect on the perceived technical competence and the decision to adopt digital forensics by Malaysian law enforcement agencies was found insignificant. Thus, these results indicated that the developed integrated framework provides an effective prediction of the digital forensics adoption by Malaysian law enforcement agencies.

Keywords: digital forensics, digital forensics adoption, digital information, law enforcement agency

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Authors: Kazunori Nomura, Hiromichi Ogi, Masaumi Nakahara, Sou Watanabe, Atsuhiro Shibata

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Chemical Processing Facility of Japan Atomic Energy Agency is a basic research field for advanced back-end technology developments with using actual high-level radioactive materials such as irradiated fuels from the fast reactor, high-level liquid waste from reprocessing plant. In the nature of a research facility, various kinds of chemical reagents have been offered for fundamental tests. Most of them were treated properly and stored in the liquid waste vessel equipped in the facility, but some were not treated and remained at the experimental space as a kind of legacy waste. It is required to treat the waste in safety. On the other hand, we formulated the Medium- and Long-Term Management Plan of Japan Atomic Energy Agency Facilities. This comprehensive plan considers Chemical Processing Facility as one of the facilities to be decommissioned. Even if the plan is executed, treatment of the “legacy” waste beforehand must be a necessary step for decommissioning operation. Under this circumstance, we launched a collaborative research project called the STRAD project, which stands for Systematic Treatment of Radioactive liquid waste for Decommissioning, in order to develop the treatment processes for wastes of the nuclear research facility. In this project, decomposition methods of chemicals causing a troublesome phenomenon such as corrosion and explosion have been developed and there is a prospect of their decomposition in the facility by simple method. And solidification of aqueous or organic liquid wastes after the decomposition has been studied by adding cement or coagulants. Furthermore, we treated experimental tools of various materials with making an effort to stabilize and to compact them before the package into the waste container. It is expected to decrease the number of transportation of the solid waste and widen the operation space. Some achievements of these studies will be shown in this paper. The project is expected to contribute beneficial waste management outcome that can be shared world widely.

Keywords: chemical processing facility, medium- and long-term management plan of JAEA facilities, STRAD project, treatment of radioactive waste

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Authors: Luis Rodrigo Valencia Perez, Francisco Flores Aguero, Gibran Aguilar Rangel

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Artificial Intelligence (AI) is rapidly transforming diverse sectors, and higher education in Latin America is no exception. This article explores the impact of AI on higher education institutions in the region, highlighting the imperative need for well-trained teachers in emerging technologies and a cultural shift towards the adoption and efficient use of these tools. AI offers significant opportunities to improve learning personalization, optimize administrative processes, and promote more inclusive and accessible education. However, the effectiveness of its implementation depends largely on the preparation and willingness of teachers to integrate these technologies into their pedagogical practices. Furthermore, it is essential that Latin American countries develop and implement public policies that encourage the adoption of AI in the education sector, thus ensuring that institutions can compete globally. Policies should focus on the continuous training of educators, investment in technological infrastructure, and the creation of regulatory frameworks that promote innovation and the ethical use of AI. Only through a comprehensive and collaborative approach will it be possible to fully harness the potential of AI to transform higher education in Latin America, thereby boosting the region's development and competitiveness on the global stage.

Keywords: artificial intelligence (AI), higher education, teacher training, public policies, latin america, global competitiveness

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Authors: Somoshree Datta, Chithra A. V., Sandeep Nithyanandan, Smitha K. K.

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Space exploration has always been at the forefront of technological innovation, pushing the boundaries of human knowledge and capabilities. In recent years, the integration of Artificial Intelligence (AI) has revolutionized the field, offering unprecedented opportunities to enhance the efficiency, autonomy and intelligence of space missions. This survey paper aims to provide a comprehensive overview of the multifaceted applications of AI in space exploration, exploring the evolution of this synergy and its impact on mission success, scientific discovery, and the future of space endeavors. Indian Space Research Organization (ISRO) has achieved great feats in the recent moon mission (Chandrayaan-3) and sun mission (Aditya L1) by using artificial intelligence to enhance moon navigation as well as help young scientists to study the Sun even before the launch by creating AI-generated image visualizations. Throughout this survey, we will review key advancements, challenges and prospects in the intersection of AI and space exploration. As humanity continues its quest to explore the cosmos, the integration of AI promises to unlock new frontiers, reshape mission architectures, and redefine our understanding of the universe. This survey aims to serve as a comprehensive resource for researchers, engineers and enthusiasts interested in the dynamic and evolving landscape of AI applications in space exploration.

Keywords: artificial intelligence, space exploration, space missions, deep learning

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Authors: Jun-ya Nagase, Toshiyuki Satoh, Norihiko Saga, Koichi Suzumori

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In recent years, Japanese society has been aging, engendering a labour shortage of young workers. Robots are therefore expected to perform tasks such as rehabilitation, nursing elderly people, and day-to-day work support for elderly people. The pneumatic balloon actuator is a rubber artificial muscle developed for use in a robot hand in such environments. This actuator has a long stroke, and a high power-to-weight ratio compared with the present pneumatic artificial muscle. Moreover, the dynamic characteristics of this actuator resemble those of human muscle. This study evaluated characteristics of force control of balloon actuator using a predictive functional control (PFC) system with disturbance observer. The predictive functional control is a model-based predictive control (MPC) scheme that predicts the future outputs of the actual plants over the prediction horizon and computes the control effort over the control horizon at every sampling instance. For this study, a 1-link finger system using a pneumatic balloon actuator is developed. Then experiments of PFC control with disturbance observer are performed. These experiments demonstrate the feasibility of its control of a pneumatic balloon actuator for a robot hand.

Keywords: disturbance observer, pneumatic balloon, predictive functional control, rubber artificial muscle

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Authors: Genady Grabarnik, Serge Yaskolko

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Generative Artificial Intelligence (AI) has significantly expanded its applicability with the incorporation of Large Language Models (LLMs) and become a technology with promise to automate some areas that were very difficult to automate before. The paper describes the introduction of generative Artificial Intelligence into Introductory Computer and Data Science courses and analysis of effect of such introduction. The generative Artificial Intelligence is incorporated in the educational process two-fold: For the instructors, we create templates of prompts for generation of tasks, and grading of the students work, including feedback on the submitted assignments. For the students, we introduce them to basic prompt engineering, which in turn will be used for generation of test cases based on description of the problems, generating code snippets for the single block complexity programming, and partitioning into such blocks of an average size complexity programming. The above-mentioned classes are run using Large Language Models, and feedback from instructors and students and courses’ outcomes are collected. The analysis shows statistically significant positive effect and preference of both stakeholders.

Keywords: introductory computer and data science education, generative AI, large language models, application of LLMS to computer and data science education

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