Search results for: recurrent artificial neural network

Authors: Catherine Constantinidis (Nee Tsacalos)

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This paper presents one aspect of the larger Masters qualitative study exploring the roles of married Greek Orthodox clergy, the Priest and Presbytera, under the wing of the Greek Orthodox Archdiocese of Australia. This ground breaking research necessitated the creation of primary data within a phenomenological paradigm drawing from lived experiences of the Priests and Presbyteres in contemporary society. As a Social Worker, a bilingual (Greek/English) Mental Health practitioner and a Presbytera, the questions constantly raised and pondered are: Who do the Priest and Presbytera turn to when they experience difficulties or problems? Where do they go for support? What is in place for their emotional and psychological health and well-being? Who cares for the spiritual carer? Who is there to catch our falling clergy and their wives? What is their 'safety net'? Identified phenomena of angst, stress, frustration and confusion experienced by the Priest and (by extension) the Presbytera, within their position, coupled with basic assumptions, perceptions and expectations about their roles, the role of the organisation (the Church), and their role as spouse often caused confusion and in some cases conflict. Unpacking this complex and multi-dimensional relationship highlighted not only the roller coaster of emotions, potentially affecting their physical and mental health, but also the impact on the interwoven relationships of marriage and ministry. The author considers these phenomena in the light of bilingual cultural and religious organisational practice frameworks, specifically the Greek Orthodox Church, whilst filtering these findings through a mental health lens. One could argue that it is an expectation that clergy (and by default their wives) take on the responsibility to be kind, nurturing and supportive to others. However, when it comes to taking care of self, they are not nearly as kind. This research looks at a recurrent theme throughout the interviews where all participants talked about limited support systems and poor self care strategies and the impact this has on their ministry, mental, emotional, and physical health and ultimately on their relationships with self and others. The struggle all participants encountered at some point in their ministry was physical, spiritual and psychological burn out. The overall aim of the researcher is to provide a voice for the Priest and the Presbytera painting a clearer picture of these roles and facilitating an awareness of struggles and dilemmas faced in their ministry. It is hoped these identified gaps in self care strategies and support systems will provide solid foundations for building a culturally sensitive, empathetic and effective support system framework, incorporating the spiritual and psychological well-being of the Priest and Presbytera, a ‘safety net’. A supplementary aim is to inform and guide ministry practice frameworks for clergy, spouses, the church hierarchy and religious organisations on a local and global platform incorporating some sort of self-care system.

Keywords: care for the carer, mental health, Priest, Presbytera, religion, support system

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Authors: Pedro G. Rodrigues, Maria J. Bárrios, Sara A. Ambrósio

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The digitalisation of health is a profoundly transformative economic, political, and social process. As is often the case, such processes need to be carefully managed if misunderstandings, policy misalignments, or outright conflicts between the government and a wide gamut of stakeholders with competing interests are to be avoided. Thus, ensuring open lines of communication where all parties know what each other’s concerns are is key to good governance, as well as efficient and effective policymaking. This project aims to make a small but still significant contribution in this regard in that we seek to determine the extent to which health managers’ perceptions of what is a priority for digital health in Portugal are aligned with official policy perspectives. By applying state-of-the-art artificial intelligence technology first to the indexed literature on digital health and then to a set of official policy documents on the same topic, followed by a survey directed at health managers working in public and private hospitals in Portugal, we obtain two priority rankings that, when compared, will allow us to produce a synthesis and toolkit on digital health policy in Portugal, with a view to identifying areas of policy convergence and divergence. This project is also particularly peculiar in the sense that sophisticated digital methods related to text analytics are employed to study good governance aspects of digitalisation applied to health care.

Keywords: digital health, health informatics, text analytics, governance, natural language understanding

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Authors: Nur Mohammad Ali, Md. Shafiqul Alam, Jayanta Bhusan Deb, Nowrin Sharmin

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The past ten years have seen significant expansion in the aviation sector, which during the previous five years has steadily pushed emerging countries closer to economic independence. It is crucial to accurately forecast the potential demand for air travel to make long-term financial plans. To forecast market demand for low-cost passenger carriers, this study suggests working with low-cost airlines, airports, consultancies, and governmental institutions' strategic planning divisions. The study aims to develop an artificial intelligence-based methods, notably fuzzy inference systems (FIS), to determine the most accurate forecasting technique for domestic low-cost carrier demand in Bangladesh. To give end users real-world applications, the study includes nine variables, two sub-FIS, and one final Mamdani Fuzzy Inference System utilizing a graphical user interface (GUI) made with the app designer tool. The evaluation criteria used in this inquiry included mean square error (MSE), accuracy, precision, sensitivity, and specificity. The effectiveness of the developed air passenger demand prediction FIS is assessed using 240 data sets, and the accuracy, precision, sensitivity, specificity, and MSE values are 90.83%, 91.09%, 90.77%, and 2.09%, respectively.

Keywords: aviation industry, fuzzy inference system, membership function, graphical user interference

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Authors: Mohit Singla, Vishavjeet Singh Hans, Sukhmeet Singh

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Heat transfer and friction characteristics of evacuated tube collector solar air heater artificially roughened with periodic circular rib of uniform cross-section were investigated. The present investigation was carried out in ANSYS Fluent 15.0 to study the impact of roughness geometry parameters, i.e. relative roughness pitch (P/e) of 8 and relative roughness height (e/Dh) of 0.064 and flow parameters, i.e. Reynolds number range of 2500-8000 on Nusselt number and friction factor. RNG k-ε with enhanced wall treatment turbulence model was selected for analysis. The results obtained for roughened evacuated tube collector has been compared with smooth evacuated tube collector for the similar flow conditions. With the increment in Reynolds number from 2500 to 8000, Nusselt number augments while friction factor decreases. Maximum enhancement ratio of Nusselt number and friction factor was 1.71 and 2.7 respectively, obtained at Reynolds number value of 8000. The value of thermo-hydraulic performance parameter was varied between 1.18 - 1.23 for the entire range of Reynolds number, indicates the advantage to use the roughened evacuated tube collector over smooth evacuated tube collector in solar air heater.

Keywords: artificial roughness, evacuated tube collector, friction factor, Nusselt number

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Authors: Farbod Rohani, Elyar Ghafoori, Matin Saeedkondori

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Impulse noise is electromagnetic emission which generated by many house hold appliances that are attached to the electrical network. The main difficulty of impulsive noise (IN) elimination process from communication channels is to distinguish it from the transmitted signal and more importantly choosing the proper threshold bandwidth in order to eliminate the signal. Because of wide band property of impulsive noise, we present a novel method for setting the detection threshold, by taking advantage of the fact that impulsive noise bandwidth is usually wider than that of typical communication channels and specifically OFDM channel. After IN detection procedure, we apply simple windowing mechanisms to eliminate them from the communication channel.

Keywords: impulsive noise, OFDM channel, threshold detecting, windowing mechanisms

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Authors: Smah Almotiri

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One of the most common fields of natural language processing (NLP) is sentimental analysis. The inferred feeling in the text can be successfully mined for various events using sentiment analysis. Twitter is viewed as a reliable data point for sentimental analytics studies since people are using social media to receive and exchange different types of data on a broad scale during the COVID-19 epidemic. The processing of such data may aid in making critical decisions on how to keep the situation under control. The aim of this research is to look at how sentimental states differed in a single geographic region during the lockdown at two different times.1162 tweets were analyzed related to the COVID-19 pandemic lockdown using keywords hashtags (lockdown, COVID-19) for the first sample tweets were from March 23, 2020, until April 23, 2020, and the second sample for the following year was from March 1, 2020, until April 4, 2020. Natural language processing (NLP), which is a form of Artificial intelligence, was used for this research to calculate the sentiment value of all of the tweets by using AFINN Lexicon sentiment analysis method. The findings revealed that the sentimental condition in both different times during the region's lockdown was positive in the samples of this study, which are unique to the specific geographical area of New Zealand. This research suggests applying machine learning sentimental methods such as Crystal Feel and extending the size of the sample tweet by using multiple tweets over a longer period of time.

Keywords: sentiment analysis, Twitter analysis, lockdown, Covid-19, AFINN, NodeJS

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Authors: Pongsawee Supanonth

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This research is the study of Spectrum Management and the increase in efficiency of Spectrum Utilization. It also proves that Cognitive Radio is a newer technology that will change the face of e-communications network today. This study used qualitative research methods by using in-depth interviews to collect data from a sample specific to those who work in Radio channel from 6 key informant and literature review from the related documents in online database. The result is the technique of Dynamic Spectrum Allocation that is the most suitable for Thailand. We conduct in-depth research for future purposes. Moreover, we can also develop a model that can be used in regulating and managing spectrum that is most suitable for Thailand. And also develop an important tool which can be of importance to allocation of spectrum as a natural resource appropriately. It will also guarantee quality and high benefit in a substantial way.

Keywords: cognitive radio, management of radio spectrum, spectrum management, spectrum scarcity

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Authors: Zunjarrao Kadam, Vikas Minchekar

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The facial recognition is an important task in criminal investigation procedure. The security guards-constantly watching the persons-can help to identify the suspected accused. The forensic psychologists are tackled such cases in the criminal justice system. The security personnel may loss their ability to correctly identify the persons due to constant stress while performing the duty. The present study aimed at to identify the effect of experimentally induced stress on facial recognition ability of security personnel’s. For this study 50, security guards from Sangli, Miraj & Jaysingpur city of the Maharashtra States of India were recruited in the experimental study. The randomized two group design was employed to carry out the research. In the initial condition twenty identity card size photographs were shown to both groups. Afterward, artificial stress was induced in the experimental group through the difficultpuzzle-solvingtask in a limited period. In the second condition, both groups were presented earlier photographs with another additional thirty new photographs. The subjects were asked to recognize the photographs which are shown earliest. The analyzed data revealed that control group has ahighest mean score of facial recognition than experimental group. The results were discussed in the present research.

Keywords: experimentally induced stress, facial recognition, cognition, security personnel

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Authors: Lucia Liu, Matthew Koziol

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Obsessive-compulsive disorder (OCD), a long-lasting anxiety disorder involving recurrent, intrusive thoughts, affects over 2 million adults in the United States. Transcranial magnetic stimulation (TMS) stands out as a noninvasive, cutting-edge therapy that has been shown to reduce symptoms in patients with treatment-resistant OCD. The Food and Drug Administration (FDA) approved protocol pairs TMS sessions with individualized symptom provocation, aiming to improve the susceptibility of brain circuits to stimulation. However, limited standardization or guidance exists on how to conduct symptom provocation and which methods are most effective. This study aims to compare the effect of internal versus external techniques to induce obsessional stress in a clinical setting during TMS therapy. Two symptom provocation methods, (i) Asking patients thought-provoking questions about their obsessions (internal) and (ii) Requesting patients to perform obsession-related tasks (external), were employed in a crossover design with repeated measurement. Thirty-six treatments of NeuroStar TMS were administered to each of two patients over 8 weeks in an outpatient clinic. Patient One received 18 sessions of internal provocation followed by 18 sessions of external provocation, while Patient Two received 18 sessions of external provocation followed by 18 sessions of internal provocation. The primary outcome was the level of self-reported obsessional stress on a visual analog scale from 1 to 10. The secondary outcome was self-reported OCD severity, collected biweekly in a four-level Likert-scale (1 to 4) of bad, fair, good and excellent. Outcomes were compared and tested between provocation arms through repeated measures ANOVA, accounting for intra-patient correlations. Ages were 42 for Patient One (male, White) and 57 for Patient Two (male, White). Both patients had similar moderate symptoms at baseline, as determined through the Yale-Brown Obsessive Compulsive Scale (YBOCS). When comparing obsessional stress induced across the two arms of internal and external provocation methods, the mean (SD) was 6.03 (1.18) for internal and 4.01 (1.28) for external strategies (P=0.0019); ranges were 3 to 8 for internal and 2 to 8 for external strategies. Internal provocation yielded 5 (31.25%) bad, 6 (33.33%) fair, 3 (18.75%) good, and 2 (12.5%) excellent responses for OCD status, while external provocation yielded 5 (31.25%) bad, 9 (56.25%) fair, 1 (6.25%) good, and 1 (6.25%) excellent responses (P=0.58). Internal symptom provocation tactics had a significantly stronger impact on inducing obsessional stress and led to better OCD status (non-significant). This could be attributed to the fact that answering questions may prompt patients to reflect more on their lived experiences and struggles with OCD. In the future, clinical trials with larger sample sizes are warranted to validate this finding. Results support the increased integration of internal methods into structured provocation protocols, potentially reducing the time required for provocation and achieving greater treatment response to TMS.

Keywords: obsessive-compulsive disorder, transcranial magnetic stimulation, mental health, symptom provocation

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Authors: Mateusz Janas

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Innovative implementations in the micro, small, and medium-sized enterprises (MSME) sector are among the essential activities considering the current market realities, technological advancements, and digitization trends. MSMEs play a crucial role and significantly influence the economic conditions of countries, as their competitiveness directly impacts the global economy. Business development and investment in innovation and technology are integral parts of every modern enterprise's strategy, seeking to maintain and achieve a desired competitive position. The instability of the socio-economic environment, along with contemporary changes in artificial intelligence implementation and digitization, requires businesses to adopt increasingly newer solutions and actions. Enterprises must strive to survive in the global market and build competitive positions, especially in uncertain conditions. Being aware of the significance of innovative actions is crucial for MSMEs as it enables them to enhance their operations and expand their scope. It is essential for managers and executives of MSMEs to be focused on development and innovation, as their approach will also impact their employees, emphasizing results and maximizing the company's value. Managers of MSMEs must be aware of various threats, costs, opportunities, and gains that can arise from implementing new technical and organizational solutions. Businesses must view development as an integral part of their strategy and continuously strive for improvement.

Keywords: innovation, SME, develop, management

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Authors: Jonghoon Kim, Gilwon Yoon

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A wrist-band type biosignal measurement system and its data transfer through human body communication (HBC) were investigated. An HBC method based on pulses of ultra-wide band instead of using frequency or amplitude modulations was studied and implemented since the system became very compact and it was more suited for personal or mobile health monitoring. Our system measured photo-plethysmogram (PPG) and measured PPG signals were transmitted through a finger to a monitoring PC system. The device was compact and low-power consuming. HBC communication has very strong security measures since it does not use wireless network. Furthermore, biosignal monitoring system becomes handy because it does not need to have wire connections.

Keywords: biosignal, human body communication, mobile health, PPG, ultrawide band

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Authors: Amit Prakash Singh, Shyamli Singh, Chavi Srivastav

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This paper presents the idea of Internet of Thing (IoT) for the infrastructure of smart cities. Internet of Thing has been visualized as a communication prototype that incorporates myriad of digital services. The various component of the smart cities shall be implemented using microprocessor, microcontroller, sensors for network communication and protocols. IoT enabled systems have been devised to support the smart city vision, of which aim is to exploit the currently available precocious communication technologies to support the value-added services for function of the city. Due to volume, variety, and velocity of data, it requires analysis using Big Data concept. This paper presented the various techniques used to analyze big data using machine learning.

Keywords: IoT, smart city, embedded systems, sustainable environment

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Authors: Sonal Purohit, Animesh Dubey

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Corporate governance deals with the entire network of formal and informal relationship with the management of the company and company’s stakeholders including employees, customers, creditors, local communities, and society in general. The recent financial crisis was truly a global crisis in its nature and effects. The Indian financial markets were not immune to this global financial crisis. It is believed that corporate governance also had a major role to play in staggering the effect of this crisis. The objective of this paper is to examine the failure of prevailing corporate governance practice in India during financial crisis. Lack of appropriate implementation of the corporate government norms was a reason behind the phenomenon of money being pulled-out by FIIs, which constitute major investors and influencers of the Indian financial market.

Keywords: corporate governance, FII, financial market, financial crisis

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Authors: Simone Leonardi, Luca Ardito

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Micro-influencers are effective elements in the marketing strategies of companies and institutions because of their capability to create an hyper-engaged audience around a specific topic of interest. In recent years, many scientific approaches and commercial tools have handled the task of detecting this type of social media users. These strategies adopt solutions ranging from rule based machine learning models to deep neural networks and graph analysis on text, images, and account information. This work compares the existing solutions and proposes an ensemble method to generalize them with different input data and social media platforms. The deployed solution combines deep learning models on unstructured data with statistical machine learning models on structured data. We retrieve both social media accounts information and multimedia posts on Twitter and Instagram. These data are mapped into feature vectors for an eXtreme Gradient Boosting (XGBoost) classifier. Sixty different topics have been analyzed to build a rule based gold standard dataset and to compare the performances of our approach against baseline classifiers. We prove the effectiveness of our work by comparing the accuracy, precision, recall, and f1 score of our model with different configurations and architectures. We obtained an accuracy of 0.91 with our best performing model.

Keywords: deep learning, gradient boosting, image processing, micro-influencers, NLP, social media

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Authors: Seyed Esmail Seyedi Bariran, Khairul Salleh Mohamed Sahari

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In robotics literature, the simultaneous localization and mapping (SLAM) is commonly associated with a priori-posteriori problem. The autonomous vehicle needs a neutral map to spontaneously track its local position, i.e., “localization” while at the same time a precise path estimation of the environment state is required for effective route planning and obstacle avoidance. On the other hand, the environmental noise factors can significantly intensify the inherent uncertainties in using odometry information and measurements obtained from the robot’s exteroceptive sensor which in return directly affect the overall performance of the corresponding SLAM. Therefore, the current work is primarily dedicated to provide a diagnostic analysis of six SLAM algorithms including FastSLAM, L-SLAM, GraphSLAM, Grid SLAM and DP-SLAM. A SLAM simulated environment consisting of two sets of landmark locations and robot waypoints was set based on modified EKF and UKF in MATLAB using two separate maps for indoor and outdoor route planning subject to natural and artificial obstacles. The simulation results are expected to provide an unbiased platform to compare the estimation performances of the five SLAM models as well as on the reliability of each SLAM model for indoor and outdoor applications.

Keywords: route planning, obstacle, estimation performance, FastSLAM, L-SLAM, GraphSLAM, Grid SLAM, DP-SLAM

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Authors: Johannes J. Schneider, Mathias S. Weyland, Peter Eggenberger Hotz, William D. Jamieson, Oliver Castell, Alessia Faggian, Rudolf M. Füchslin

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Agglomerations of polydisperse systems of spherical particles are created in computer simulations using a simplified stochastic-hydrodynamic model: Particles sink to the bottom of the cylinder, taking into account gravity reduced by the buoyant force, the Stokes friction force, the added mass effect, and random velocity changes. Two types of particles are considered, with one of them being able to create connections to neighboring particles of the same type, thus forming a network within the agglomeration at the bottom of a cylinder. Decreasing the fraction of these particles, a percolation transition occurs. The critical regime is determined by investigating the maximum cluster size and the percolation susceptibility.

Keywords: binary system, maximum cluster size, percolation, polydisperse

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Authors: Anjali Chouksey, Riya Nimje, Jahanvi Saraf

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Online transactions have increased dramatically in this new ‘social-distancing’ era. With online transactions, Fraud in online payments has also increased significantly. Frauds are a significant problem in various industries like insurance companies, baking, etc. These frauds include leaking sensitive information related to the credit card, which can be easily misused. Due to the government also pushing online transactions, E-commerce is on a boom. But due to increasing frauds in online payments, these E-commerce industries are suffering a great loss of trust from their customers. These companies are finding credit card fraud to be a big problem. People have started using online payment options and thus are becoming easy targets of credit card fraud. In this research paper, we will be discussing machine learning algorithms. We have used a decision tree, XGBOOST, k-nearest neighbour, logistic-regression, random forest, and SVM on a dataset in which there are transactions done online mode using credit cards. We will test all these algorithms for detecting fraud cases using the confusion matrix, F1 score, and calculating the accuracy score for each model to identify which algorithm can be used in detecting frauds.

Keywords: machine learning, fraud detection, artificial intelligence, decision tree, k nearest neighbour, random forest, XGBOOST, logistic regression, support vector machine

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Authors: Ahmed Elsayed, Andrew Shea, Nicolas Kelly, John Allison

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In northern European climates, domestic space heating and hot water represents a significant proportion of total primary total primary energy use and meeting these demands from a national electricity grid network supplied by renewable energy sources provides an opportunity for a significant reduction in EU CO₂ emissions. However, in order to adapt to the intermittent nature of renewable energy generation and to avoid co-incident peak electricity usage from consumers that may exceed current capacity, the demand for heat must be decoupled from its generation. Storage of heat within the fabric of dwellings for use some hours, or days, later provides a route to complete decoupling of demand from supply and facilitates the greatly increased use of renewable energy generation into a local or national electricity network. The integration of thermal energy storage into the building fabric for retrieval at a later time requires much evaluation of the many competing thermal, physical, and practical considerations such as the profile and magnitude of heat demand, the duration of storage, charging and discharging rate, storage media, space allocation, etc. In this paper, the authors report investigations of thermal storage in building fabric using concrete material and present an evaluation of several factors that impact upon performance including heating pipe layout, heating fluid flow velocity, storage geometry, thermo-physical material properties, and also present an investigation of alternative storage materials and alternative heat transfer fluids. Reducing the heating pipe spacing from 200 mm to 100 mm enhances the stored energy by 25% and high-performance Vacuum Insulation results in heat loss flux of less than 3 W/m², compared to 22 W/m² for the more conventional EPS insulation. Dense concrete achieved the greatest storage capacity, relative to medium and light-weight alternatives, although a material thickness of 100 mm required more than 5 hours to charge fully. Layers of 25 mm and 50 mm thickness can be charged in 2 hours, or less, facilitating a fast response that could, aggregated across multiple dwellings, provide significant and valuable reduction in demand from grid-generated electricity in expected periods of high demand and potentially eliminate the need for additional new generating capacity from conventional sources such as gas, coal, or nuclear.

Keywords: fabric integrated thermal storage, FITS, demand side management, energy storage, load shifting, renewable energy integration

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Authors: Lin Zhang

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Neighborhood redevelopment is vital to improve residents’ living environment, and there has been a national neighborhood micro-redevelopment initiative in China since 2020, which is largely different from the previous large-scale demolition and reconstruction projects. Yet, few studies systematically examine the new interactions of multiple actors in this initiative. China’s neighborhood (micro-) redevelopment is a kind of governance network, and the complexity perspective could reflect the dynamic nature of multiple actors and their relationships in governance networks. In order to better understand the fundamental shifts of governance networks in China’s neighborhood micro-redevelopment, this paper adopted a theoretical framework of complexity in governance networks and analyzed the new governance networks of neighborhood micro-redevelopment projects in Haikou accordingly.

Keywords: neighborhood redevelopment, governance, networks, Haikou

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Authors: Jyoti Sharma

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India and Pakistan are two strategically important neighboring countries in Asia-Pacific region. Since independence of more than six decades, both, India and Pakistan have transverse different paths, India as a Sovereign, Democratic, Republic Country and Pakistan as Islamic Republic of Pakistan. The advent of democracy in India and Islamic republic in Pakistan resulted in new hopes, aspirations and demands on education. During the six decades after Independence, teacher education in both countries has come a long way from its initial bleak stature to gain an identity as a complex network of institutions and programs. The present paper takes a close look into the paradigm shift in teacher education programs in India and Pakistan and how much the shift is influenced by constitutional frameworks of each country.

Keywords: pre-service teachers, teacher education reforms, India, Pakistan

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Authors: Maik Kschischo, Dominik Kahl, Philipp Wendland, Andreas Weber

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Understanding and modelling of real-world complex dynamic systems in biology, engineering and other fields is often made difficult by incomplete knowledge about the interactions between systems states and by unknown disturbances to the system. In fact, most real-world dynamic networks are open systems receiving unknown inputs from their environment. To understand a system and to estimate the state dynamics, these inputs need to be reconstructed from output measurements. Reconstructing the input of a dynamic system from its measured outputs is an ill-posed problem if only a limited number of states is directly measurable. A first requirement for solving this problem is the invertibility of the input-output map. In our work, we exploit the fact that invertibility of a dynamic system is a structural property, which depends only on the network topology. Therefore, it is possible to check for invertibility using a structural invertibility algorithm which counts the number of node disjoint paths linking inputs and outputs. The algorithm is efficient enough, even for large networks up to a million nodes. To understand structural features influencing the invertibility of a complex dynamic network, we analyze synthetic and real networks using the structural invertibility algorithm. We find that invertibility largely depends on the degree distribution and that dense random networks are easier to invert than sparse inhomogeneous networks. We show that real networks are often very difficult to invert unless the sensor nodes are carefully chosen. To overcome this problem, we present a sensor node placement algorithm to achieve invertibility with a minimum set of measured states. This greedy algorithm is very fast and also guaranteed to find an optimal sensor node-set if it exists. Our results provide a practical approach to experimental design for open, dynamic systems. Since invertibility is a necessary condition for unknown input observers and data assimilation filters to work, it can be used as a preprocessing step to check, whether these input reconstruction algorithms can be successful. If not, we can suggest additional measurements providing sufficient information for input reconstruction. Invertibility is also important for systems design and model building. Dynamic models are always incomplete, and synthetic systems act in an environment, where they receive inputs or even attack signals from their exterior. Being able to monitor these inputs is an important design requirement, which can be achieved by our algorithms for invertibility analysis and sensor node placement.

Keywords: data-driven dynamic systems, inversion of dynamic systems, observability, experimental design, sensor node placement

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Authors: Rômulo Eustáquio Martins de Souza, Paulo Alexandre Rodrigues de Vasconcelos Figueiredo

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In recent years, there has been a surge in interest in applying artificial intelligence techniques, particularly machine learning algorithms. Machine learning is a data-analysis technique that automates the creation of analytical models, making it especially useful for designing complex situations. As a result, this technology aids in reducing human intervention while producing accurate results. This methodology is also extensively used in aerospace engineering since this is a field that encompasses several high-complexity operations, such as rocket propulsion. Rocket propulsion is a high-risk operation in which engine failure could result in the loss of life. As a result, it is critical to use computational methods capable of precisely representing the spacecraft's analytical model to guarantee its security and operation. Thus, this paper describes the use of machine learning algorithms for rocket propulsion to aid the realization that this technique is an efficient way to deal with challenging and restrictive aerospace engineering activities. The paper focuses on three machine-learning-aided rocket propulsion applications: set-point control of an expander-bleed rocket engine, supersonic retro-propulsion of a small-scale rocket, and leak detection and isolation on rocket engine data. This paper describes the data-driven methods used for each implementation in depth and presents the obtained results.

Keywords: data analysis, modeling, machine learning, aerospace, rocket propulsion

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Authors: Ebrahim Alrashed

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Wireless sensor networks are vulnerable to attacks from adversaries attempting to disrupt their operations. Sink-hole attacks are a type of attack where an adversary node drops data forwarded through it and hence affecting the reliability and accuracy of the network. Since sensor nodes have limited battery power, it is essential that any solution to the sinkhole attack problem be very energy-aware. In this paper, we present a reliable and energy efficient scheme to forward data from source nodes to the base station while under sink-hole attack. The scheme also detects sink-hole attack nodes and avoid paths that includes them.

Keywords: energy-aware routing, reliability, sink-hole attack, WSN

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Authors: Najmeh Abedzadeh, Matthew Jacobs

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An intrusion detection system (IDS) is a software application that monitors malicious activities and generates alerts if any are detected. However, most network activities in IDS datasets are normal, and the relatively few numbers of attacks make the available data imbalanced. Consequently, cyber-attacks can hide inside a large number of normal activities, and machine learning algorithms have difficulty learning and classifying the data correctly. In this paper, a comprehensive literature review is conducted on different types of algorithms for both implementing the IDS and methods in correcting the imbalanced IDS dataset. The most famous algorithms are machine learning (ML), deep learning (DL), synthetic minority over-sampling technique (SMOTE), and reinforcement learning (RL). Most of the research use the CSE-CIC-IDS2017, CSE-CIC-IDS2018, and NSL-KDD datasets for evaluating their algorithms.

Keywords: IDS, imbalanced datasets, sampling algorithms, big data

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Authors: RAM PAL SINGH, VIKASH CHAUDHARY, MONIKA VERMA

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In this paper, we proposed the implementation of optimization based Extreme Learning Machine (ELM) for watermarking of B-channel of color image in discrete wavelet transform (DWT) domain. ELM, a regularization algorithm, works based on generalized single-hidden-layer feed-forward neural networks (SLFNs). However, hidden layer parameters, generally called feature mapping in context of ELM need not to be tuned every time. This paper shows the embedding and extraction processes of watermark with the help of ELM and results are compared with already used machine learning models for watermarking.Here, a cover image is divide into suitable numbers of non-overlapping blocks of required size and DWT is applied to each block to be transformed in low frequency sub-band domain. Basically, ELM gives a unified leaning platform with a feature mapping, that is, mapping between hidden layer and output layer of SLFNs, is tried for watermark embedding and extraction purpose in a cover image. Although ELM has widespread application right from binary classification, multiclass classification to regression and function estimation etc. Unlike SVM based algorithm which achieve suboptimal solution with high computational complexity, ELM can provide better generalization performance results with very small complexity. Efficacy of optimization method based ELM algorithm is measured by using quantitative and qualitative parameters on a watermarked image even though image is subjected to different types of geometrical and conventional attacks.

Keywords: BER, DWT, extreme leaning machine (ELM), PSNR

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Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Pedro Paulo Galindo Francisco, Eli Dhadad Junior

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This study addresses the Meese-Rogoff Puzzle by introducing the latest machine learning techniques as alternatives for predicting the exchange rates. Using RMSE as a comparison metric, Meese and Rogoff discovered that economic models are unable to outperform the random walk model as short-term exchange rate predictors. Decades after this study, no statistical prediction technique has proven effective in overcoming this obstacle; although there were positive results, they did not apply to all currencies and defined periods. Recent advancements in artificial intelligence technologies have paved the way for a new approach to exchange rate prediction. Leveraging this technology, we applied five machine learning techniques to attempt to overcome the Meese-Rogoff puzzle. We considered daily data for the real, yen, British pound, euro, and Chinese yuan against the US dollar over a time horizon from 2010 to 2023. Our results showed that none of the presented techniques were able to produce an RMSE lower than the Random Walk model. However, the performance of some models, particularly LSTM and N-BEATS were able to outperform the ARIMA model. The results also suggest that machine learning models have untapped potential and could represent an effective long-term possibility for overcoming the Meese-Rogoff puzzle.

Keywords: exchage rate, prediction, machine learning, deep learning

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Authors: Saliha Ozcan

Abstract:

Technology has become an integral part of our lives, aiding individuals in accessing higher order competencies, such as global awareness, creativity, collaborative problem solving, and self-directed learning. Students need to acquire these competencies, often referred to as 21st century skills, in order to adapt to a fast changing world. Today, an ever-increasing number of schools are exploring how engagement through telecollaboration can support language learning and promote 21st century skill development in classrooms. However, little is known regarding how telecollaboration may influence the way students acquire 21st century skills. In this paper, we aim to shed light to the potential implications of telecollaborative practices in acquisition of 21st century skills. In our context, telecollaboration, which might be carried out in a variety of settings both synchronously or asynchronously, is considered as the process of communicating and working together with other people or groups from different locations through online digital tools or offline activities to co-produce a desired work output. The study presented here will describe and analyse the implementation of a telecollaborative project between two high school classes, one in Spain and the other in Sweden. The students in these classes were asked to carry out some joint activities, including creating an online platform, aimed at raising awareness of the situation of the Syrian refugees. We conduct a qualitative study in order to explore how language, culture, communication, and technology merge into the co-construction of knowledge, as well as supporting the attainment of the 21st century skills needed for network-mediated communication. To this end, we collected a significant amount of audio-visual data, including video recordings of classroom interaction and external Skype meetings. By analysing this data, we verify whether the initial pedagogical design and intended objectives of the telecollaborative project coincide with what emerges from the actual implementation of the tasks. Our findings indicate that, as well as planned activities, unplanned classroom interactions may lead to acquisition of certain 21st century skills, such as collaborative problem solving and self-directed learning. This work is part of a wider project (KONECT, EDU2013-43932-P; Spanish Ministry of Economy and Finance), which aims to explore innovative, cross-competency based teaching that can address the current gaps between today’s educational practices and the needs of informed citizens in tomorrow’s interconnected, globalised world.

Keywords: 21st century skills, telecollaboration, language learning, network mediated communication

Procedia PDF Downloads 126

Authors: Nay Myo Sandar

Abstract:

With the growth of technologies and applications, a large amount of data has been producing at increasing rate from various resources such as social media networks, sensor devices, and other information serving devices. This large collection of massive, complex and exponential growth of dataset is called big data. The traditional database systems cannot store and process such data due to large and complexity. Consequently, cloud computing is a potential solution for data storage and processing since it can provide a pool of resources for servers and storage. However, moving large amount of data to and from is a challenging issue since it can encounter a high latency due to large data size. With respect to big data movement problem, this paper reviews the literature of previous works, discusses about research issues, finds out approaches for dealing with big data movement problem.

Keywords: Big Data, Cloud Computing, Big Data Movement, Network Techniques

Procedia PDF Downloads 88

Authors: Gagnesh Kumar, Nitin Gupta

Abstract:

Ternary Content Addressable Memory cells are mainly popular in network routers for packet forwarding and packet classification, but they are also useful in a variety of other applications that require high-speed table look-up. The main TCAM-design challenge is to decrease the power consumption associated with the large amount of parallel active circuitry, without compromising with speed or memory density. Furthermore, when the channel length decreases, leakage power becomes more significant, and it can even dominate dynamic power at lower technologies. In this paper, we propose a TCAM-design technique, called Virtual Power Supply technique that reduces the leakage by a substantial amount.

Keywords: match line (ML), search line (SL), ternary content addressable memory (TCAM), Leakage power (LP)

Procedia PDF Downloads 300