Search results for: face recognition system

Authors: Bernadette F. De La Cruz, Susan Marie R. Dela Cruz, Georgia D. Demavibas

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The Expanded Student Grant-in-Aid Program for Poverty Alleviation (ESGP-PA) is a government program that aims to contribute to the National Government’s thrusts in effectively addressing poverty alleviation by increasing the number of graduates in higher education among indigent households and to get these graduates employed in in-demand occupations in order to lift their families out of poverty. Higher education continues to see an influx of these students from poor families that have never previously sent anyone to college. There are many challenges that face college students at all levels, but these are special challenges for first-generation students. Challenges that face these students can include lack of interest in attending school, low aptitude, being not single anymore, factors such as unfamiliarity with college expectations, lack of preparations while in secondary school, and limited support from family members. This research looks at some of the challenges first-generation college students face and examines the impact of these challenges on student’s aspirations for the attainment of a college degree and ultimately a high-paying career.

Keywords: ESGP-PA, first-generation college students, low aptitude, poverty alleviation

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Authors: Ahmad Abdul-Razzak Aboudi Al-Issa

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Thermal is the main important aspect in any hydraulic system since it is affected on the hydraulic system performance. Therefore must be simulated the hydraulic system -that was designed- in this aspect before constructing it. In this study, an existed expert system was using to simulate the thermal aspect of a designed hydraulic system that will be used in an industrial field. The expert system which is used in this study is (Hydraulic System Calculations), and its symbol (HSC). HSC had been designed and coded in an interactive program userfriendly named (Microsoft Visual Basic 2010).

Keywords: fluid power, hydraulic system, thermal and hydrodynamic, expert system

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Authors: Michael Moeti, Khuliso Sigama, Thapelo Samuel Matlala

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One of the principal challenges that the world is confronted with is insecurity. The crime rate is increasing exponentially, and protecting our physical assets especially in the motorist industry, is becoming impossible when applying our own strength. The need to develop technological solutions that detect and report theft without any human interference is inevitable. This is critical, especially for vehicle owners, to ensure theft detection and speedy identification towards recovery efforts in cases where a vehicle is missing or attempted theft is taking place. The vehicle theft detection system uses Convolutional Neural Network (CNN) to recognize the driver's face captured using an installed mobile phone device. The location identification function uses a Global Positioning System (GPS) to determine the real-time location of the vehicle. Upon identification of the location, Global System for Mobile Communications (GSM) technology is used to report or notify the vehicle owner about the whereabouts of the vehicle. The installed mobile app was implemented by making use of python as it is undoubtedly the best choice in machine learning. It allows easy access to machine learning algorithms through its widely developed library ecosystem. The graphical user interface was developed by making use of JAVA as it is better suited for mobile development. Google's online database (Firebase) was used as a means of storage for the application. The system integration test was performed using a simple percentage analysis. Sixty (60) vehicle owners participated in this study as a sample, and questionnaires were used in order to establish the acceptability of the system developed. The result indicates the efficiency of the proposed system, and consequently, the paper proposes the use of the system can effectively monitor the vehicle at any given place, even if it is driven outside its normal jurisdiction. More so, the system can be used as a database to detect, locate and report missing vehicles to different security agencies.

Keywords: CNN, location identification, tracking, GPS, GSM

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Authors: Omar Dib, Rita Yaacoub, Luc Eveleigh, Nathalie Locquet, Hussein Dib, Ali Bassal, Christophe B. Y. Cordella

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The potential of front-face fluorescence coupled with chemometric techniques, namely parallel factor analysis (PARAFAC) and multiple linear regression (MLR) as a rapid analysis tool to characterize Lebanese virgin olive oils was investigated. Fluorescence fingerprints were acquired directly on 102 Lebanese virgin olive oil samples in the range of 280-540 nm in excitation and 280-700 nm in emission. A PARAFAC model with seven components was considered optimal with a residual of 99.64% and core consistency value of 78.65. The model revealed seven main fluorescence profiles in olive oil and was mainly associated with tocopherols, polyphenols, chlorophyllic compounds and oxidation/hydrolysis products. 23 MLR regression models based on PARAFAC scores were generated, the majority of which showed a good correlation coefficient (R > 0.7 for 12 predicted variables), thus satisfactory prediction performances. Acid values, peroxide values, and Delta K had the models with the highest predictions, with R values of 0.89, 0.84 and 0.81 respectively. Among fatty acids, linoleic and oleic acids were also highly predicted with R values of 0.8 and 0.76, respectively. Factors contributing to the model's construction were related to common fluorophores found in olive oil, mainly chlorophyll, polyphenols, and oxidation products. This study demonstrates the interest of front-face fluorescence as a promising tool for quality control of Lebanese virgin olive oils.

Keywords: front-face fluorescence, Lebanese virgin olive oils, multiple Linear regressions, PARAFAC analysis

Procedia PDF Downloads 442

Authors: Eduardo Kanagushiku Pereira, Frank Gregory Cavalcante da Silva, Barbara Soares Gonçalves, Ana Lúcia Bergamasco Galastri, Ronei Luciano Mamoni

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The inflammatory response initiates after the recognition of pathogens by receptors expressed by innate immune cells. Among these receptors, the NLRP3 was associated with the recognition of pathogenic fungi in experimental models. NLRP3 operates forming a multiproteic complex called inflammasome, which actives caspase-1, responsible for the production of the inflammatory cytokines IL-1beta and IL-18. In this study, we aimed to investigate the involvement of NLRP3 in the inflammatory response elicited in macrophages against Paracoccidioides brasiliensis (Pb), the etiologic agent of PCM. Macrophages were differentiated from THP-1 cells by treatment with phorbol-myristate-acetate. Following differentiation, macrophages were stimulated by Pb yeast cells for 24 hours, after previous treatment with specific NLRP3 (3,4-methylenedioxy-beta-nitrostyrene) and/or caspase-1 (VX-765) inhibitors, or specific inhibitors of pathways involved in NLRP3 activation such as: Reactive Oxigen Species (ROS) production (N-Acetyl-L-cysteine), K+ efflux (Glibenclamide) or phagossome acidification (Bafilomycin). Quantification of IL-1beta and IL-18 in supernatants was performed by ELISA. Our results showed that the production of IL-1beta and IL-18 by THP-1-derived-macrophages stimulated with Pb yeast cells was dependent on NLRP3 and caspase-1 activation, once the presence of their specific inhibitors diminished the production of these cytokines. Furthermore, we found that the major pathways involved in NLRP3 activation, after Pb recognition, were dependent on ROS production and K+ efflux. In conclusion, our results showed that NLRP3 participates in the recognition of Pb yeast cells by macrophages, leading to the activation of the NLRP3-inflammasome and production of IL-1beta and IL-18. Together, these cytokines can induce an inflammatory response against P. brasiliensis, essential for the establishment of the initial inflammatory response and for the development of the subsequent acquired immune response.

Keywords: inflammation, IL-1beta, IL-18, NLRP3, Paracoccidioidomycosis

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Authors: Joel Niklaus, Matthias Sturmer

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The publication of judicial proceedings is a cornerstone of many democracies. It enables the court system to be made accountable by ensuring that justice is made in accordance with the laws. Equally important is privacy, as a fundamental human right (Article 12 in the Declaration of Human Rights). Therefore, it is important that the parties (especially minors, victims, or witnesses) involved in these court decisions be anonymized securely. Today, the anonymization of court decisions in Switzerland is performed either manually or semi-automatically using primitive software. While much research has been conducted on anonymization for tabular data, the literature on anonymization for unstructured text documents is thin and virtually non-existent for court decisions. In 2019, it has been shown that manual anonymization is not secure enough. In 21 of 25 attempted Swiss federal court decisions related to pharmaceutical companies, pharmaceuticals, and legal parties involved could be manually re-identified. This was achieved by linking the decisions with external databases using regular expressions. An automated re-identification system serves as an automated test for the safety of existing anonymizations and thus promotes the right to privacy. Manual anonymization is very expensive (recurring annual costs of over CHF 20M in Switzerland alone, according to an estimation). Consequently, many Swiss courts only publish a fraction of their decisions. An automated anonymization system reduces these costs substantially, further leading to more capacity for publishing court decisions much more comprehensively. For the re-identification system, topic modeling with latent dirichlet allocation is used to cluster an amount of over 500K Swiss court decisions into meaningful related categories. A comprehensive knowledge base with publicly available data (such as social media, newspapers, government documents, geographical information systems, business registers, online address books, obituary portal, web archive, etc.) is constructed to serve as an information hub for re-identifications. For the actual re-identification, a general-purpose language model is fine-tuned on the respective part of the knowledge base for each category of court decisions separately. The input to the model is the court decision to be re-identified, and the output is a probability distribution over named entities constituting possible re-identifications. For the anonymization system, named entity recognition (NER) is used to recognize the tokens that need to be anonymized. Since the focus lies on Swiss court decisions in German, a corpus for Swiss legal texts will be built for training the NER model. The recognized named entities are replaced by the category determined by the NER model and an identifier to preserve context. This work is part of an ongoing research project conducted by an interdisciplinary research consortium. Both a legal analysis and the implementation of the proposed system design ESRA will be performed within the next three years. This study introduces the system design of ESRA, an end-to-end system for re-identification and anonymization of Swiss court decisions. Firstly, the re-identification system tests the safety of existing anonymizations and thus promotes privacy. Secondly, the anonymization system substantially reduces the costs of manual anonymization of court decisions and thus introduces a more comprehensive publication practice.

Keywords: artificial intelligence, courts, legal tech, named entity recognition, natural language processing, ·privacy, topic modeling

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Authors: K. Prabhu, K. Dinesh, M. Ranjani, M. Suhitha

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During the tough times of covid, those people who were hospitalized found it difficult to always convey what they wanted to or needed to the attendee. Sometimes the attendees might also not be there. In that case, the patients can use simple hand gestures to control electrical appliances (like its set it for a zero watts bulb)and three other gestures for voice note intimation. In this AI-based hand recognition project, NodeMCU is used for the control action of the relay, and it is connected to the firebase for storing the value in the cloud and is interfaced with the python code via raspberry pi. For three hand gestures, a voice clip is added for intimation to the attendee. This is done with the help of Google’s text to speech and the inbuilt audio file option in the raspberry pi 4. All the five gestures will be detected when shown with their hands via the webcam, which is placed for gesture detection. The personal computer is used for displaying the gestures and for running the code in the raspberry pi imager.

Keywords: nodeMCU, AI technology, gesture, patient

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Authors: Sonia Mannan, M. Jobair Alam

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The concept of palliative care in Bangladesh largely remained limited to the sympathetic caring of patients with a life-limiting illness. Quality of Life (QoL) issues are rarely practiced in Bangladesh. Furthermore, palliative medicine, in the perspective of holistic palliative care service, does not have its proper recognition in Bangladesh. Apart from those socio-medical aspects, palliative care patients face legal issues that impact their quality of life, including access to health services and social benefits and dealing with other life-transactions of the patients and their families (such as disposing of property; planning for children). This paper is an attempt to articulate these legal dimensions of the right to palliative care in the context of Bangladesh. The major focus of this paper will be founded on the doctrinal analysis of the constitutional provisions and other relevant legislation on the right to health and their judicial interpretation, which is argued to offer a meaningful space for the right to palliative care. This paper will also investigate the gaps in the said legal framework to better secure such care. In conclusion, a few recommendations are made so that the palliative care practices in Bangladesh are better aligned with international standards, and it can respond more humanely to the patients who need palliative care.

Keywords: Bangladesh, constitution, legal regime, palliative care, quality of life

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Authors: Ayşen Melda Çolak, Volkan Okatan, Ercan Yıldız

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In our country, fruit production was determined as 17.2 million tons in 2011 according to official data. Turkey fig, apricot, cherry and quince production ranks first in the world. Almost all the regions of our country, despite the growing of fruit 54% of the total fruit production occur in the Mediterranean and the Aegean Region. However, fruit production in the country is consumed in the domestic market and export rates are often very low. In this study, a questionnaire to 100 farmers face-to-face interview. According to the survey, 40% of those in fruit and 7 da of 7 hectares land are small. 30% of soil testing for manufacturers, testing for 20% of the water. Manufacturers who deliberately fertilization rate of only 10%.

Keywords: fruit, generation, potential, Sivasli survey

Procedia PDF Downloads 247

Authors: Seynabou Toure, Oumar Diop, Kidiyo Kpalma, Amadou S. Maiga

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Color and texture are the two most determinant elements for perception and recognition of the objects in an image. For this reason, color and texture analysis find a large field of application, for example in image classification and segmentation. But, the pioneering work in texture analysis was conducted on grayscale images, thus discarding color information. Many grey-level texture descriptors have been proposed and successfully used in numerous domains for image classification: face recognition, industrial inspections, food science medical imaging among others. Taking into account color in the definition of these descriptors makes it possible to better characterize images. Color texture is thus the subject of recent work, and the analysis of color texture images is increasingly attracting interest in the scientific community. In optical remote sensing systems, sensors measure separately different parts of the electromagnetic spectrum; the visible ones and even those that are invisible to the human eye. The amounts of light reflected by the earth in spectral bands are then transformed into grayscale images. The primary natural colors Red (R) Green (G) and Blue (B) are then used in mixtures of different spectral bands in order to produce RGB images. Thus, good color texture discrimination can be achieved using RGB under controlled illumination conditions. Some previous works investigate the effect of using different color space for color texture classification. However, the selection of the best performing color space in land-sea segmentation is an open question. Its resolution may bring considerable improvements in certain applications like coastline detection, where the detection result is strongly dependent on the performance of the land-sea segmentation. The aim of this paper is to present the results of a study conducted on different color spaces in order to show the best-performing color space for land-sea segmentation. In this sense, an experimental analysis is carried out using five different color spaces (RGB, XYZ, Lab, HSV, YCbCr). For each color space, the Haar wavelet decomposition is used to extract different color texture features. These color texture features are then used for Fusion of Over Segmentation (FOOS) based classification; this allows segmentation of the land part from the sea one. By analyzing the different results of this study, the HSV color space is found as the best classification performance while using color and texture features; which is perfectly coherent with the results presented in the literature.

Keywords: classification, coastline, color, sea-land segmentation

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Authors: Milagros Jaén-Vargas, Javier García Martínez, Karla Miriam Reyes Leiva, María Fernanda Trujillo-Guerrero, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmedo

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Human Activity Recognition (HAR) is having a growing impact in creating new applications and is responsible for emerging new technologies. Also, the use of wearable sensors is an important key to exploring the human body's behavior when performing activities. Hence, the use of these dispositive is less invasive and the person is more comfortable. In this study, a database that includes three activities is used. The activities were acquired from inertial measurement unit sensors (IMU) and motion capture systems (MOCAP). The main objective is differentiating the performance from four Deep Learning (DL) models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and hybrid model Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), when considering acceleration, velocity and position and evaluate if integrating the IMU acceleration to obtain velocity and position represent an increment in performance when it works as input to the DL models. Moreover, compared with the same type of data provided by the MOCAP system. Despite the acceleration data is cleaned when integrating, results show a minimal increase in accuracy for the integrated signals.

Keywords: HAR, IMU, MOCAP, acceleration, velocity, position, feature maps

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Authors: A. N. Paithane, D. S. Bormane, S. D. Shirbahadurkar

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It has been seen that emotion recognition is an important research topic in the field of Human and computer interface. A novel technique for Feature Extraction (FE) has been presented here, further a new method has been used for human emotion recognition which is based on HHT method. This method is feasible for analyzing the nonlinear and non-stationary signals. Each signal has been decomposed into the IMF using the EMD. These functions are used to extract the features using fission and fusion process. The decomposition technique which we adopt is a new technique for adaptively decomposing signals. In this perspective, we have reported here potential usefulness of EMD based techniques.We evaluated the algorithm on Augsburg University Database; the manually annotated database.

Keywords: intrinsic mode function (IMF), Hilbert-Huang transform (HHT), empirical mode decomposition (EMD), emotion detection, electrocardiogram (ECG)

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Authors: Therese Scali

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Over the years, the European Union has continued to extend its action on lesbian, gay men, bisexual and transgender (LGBT) rights to a range of areas including access to justice, asylum, freedom of expression and assembly, parenting, and mutual recognition of civil status within the EU. The European Parliament has been a driving force behind such action adopting a range of resolutions calling for continued progress in this field. In particular, Belgium has been one of the first countries to legalize same-sex parenting and to create a general framework for action against negative attitudes towards gay and lesbian parents. The present paper aims at highlighting public healthcare workers’ attitudes towards different types of same-sex headed families in Belgium, and the content of their interventions in schools. Results revealed that attitudes can go from supportive to unsupportive, and participants do not show the same degree of support towards the different types of same-sex parenting. This contribution highlights work’s implication for public policy by understanding the resources and challenges that health-care professionals face in their work.

Keywords: attitudes, gay and lesbian parents, health-care workers, homophobia, prevention

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Authors: Hasan Alrashid

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The 2005 Hague Convention seeks to ensure legal certainty and predictability between parties in international business transactions. It harmonies exclusive choice of court agreements at the international level between parties to commercial transactions and to govern the recognition and enforcement of judgments resulting from proceedings based on such agreements to promote international trade and investment. Although the choice of court agreements is significant in international business transactions, the United Arab Emirates refuse to recognise it by Article 24 of the Federal Law No. 11 of 1992 of the Civil Procedure Code. A review of judicial judgments in United Arab Emirates up to the present day has revealed that several cases appeared before the Court in different states of United Arab Emirates regarding the recognition of exclusive choice of court agreements. In all the cases, the courts regarded the exclusive choice of court agreements as a direct assault on state authority and sovereignty and refused categorically to recognize choice of court agreements by refusing to stay proceedings in favor of the foreign chosen court. This has created uncertainty and unpredictability in international business transaction in the United Arab Emirates. In June 2011, the first Gulf Judicial Seminar on Cross-Frontier Legal Cooperation in Civil and Commercial Matters was held in Doha, Qatar. The Permanent Bureau of the Hague Conference attended the conference and invited the states of the Gulf Cooperation Council (GCC) namely, The United Arab Emirates, Bahrain, Saudi Arabia, Oman, Qatar and Kuwait to adopt some of the Hague Conventions, one of which was the Hague Convention on Choice of Court Agreements. One of the recommendations of the conference was that the GCC states should research ‘the benefits of predictability and legal certainty provided by the 2005 Convention on Choice of Court Agreements and its resulting advantages for cross-border trade and investment’ for possible adoption of the Hague Convention. Up to today, no further step has been taken by the any of the GCC states to adapt the Hague Convention nor did they conduct research on the benefits of predictability and legal certainty in international business transactions. This paper will argue that the approach regarding the recognition of choice of court agreements in United Arab Emirates states can be improved in order to help the parties in international business transactions avoid parallel litigation and ensure legal certainty and predictability. The focus will be the uncertainty and gaps regarding the choice of court agreements in the United Arab Emirates states. The Hague Convention on choice of court agreements and the importance of harmonisation of the rules of choice of court agreements at international level will also be discussed. Finally, The feasibility and desirability of recognizing choice of court agreements in United Arab Emirates legal system by becoming a party to the Hague Convention will be evaluated.

Keywords: choice of court agreements, party autonomy, public authority, sovereignty

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Authors: Kevin Montanez, Patricia Garcia

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The confinement and restrictions adopted to avoid an exponential spread of the COVID-19 have negatively impacted the Peruvian economy. In this context, Industries offering essential products could continue operating, but they have to follow safety protocols and implement strategies to ensure employee health. In view of the increasing internet access and mobile phone ownership, “Alerta Temprana”, a mobile app, was developed to self-report COVID-19 symptoms in the work context. In this study, the usability of the mobile app “Alerta Temprana” was evaluated from the perspective of health monitors and workers. In addition to reporting the metrics related to the usability of the application, the utility of the system is also evaluated from the monitors' perspective. In this descriptive study, the participants used the mobile app for two months. Afterwards, System Usability Scale (SUS) questionnaire was answered by the workers and monitors. A Usefulness questionnaire with open questions was also used for the monitors. The data related to the use of the application was collected during one month. Furthermore, descriptive statistics and bivariate analysis were used. The workers rated the application as good (70.39). In the case of the monitors, usability was excellent (83.0). The most important feature for the monitors were the emails generated by the application. The average interaction per user was 30 seconds and a total of 6172 self-reports were sent. Finally, a statistically significant association was found between the acceptability scale and the work area. The results of this study suggest that Alerta Temprana has the potential to be used for surveillance and health monitoring in any context of face-to-face modality. Participants reported a high degree of ease of use. However, from the perspective of workers, SUS cannot diagnose usability issues and we suggest we use another standard usability questionnaire to improve "Alerta Temprana" for future use.

Keywords: public health in informatics, mobile app, usability, self-report

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Authors: Shaikh Farhad Hossain, Liakot Ali

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Technology advances accelerate the quality and type of services provided for health care and especially for monitoring health conditions. Sensors have turned out to be more effective to detect diverse physiological signs and can be worn on the human body utilizing remote correspondence modules. An assortment of programming devices have been created to help in preparing a difference rundown of essential signs by examining and envisioning information produced by different sensors. In this proposition, we presented a Health signs and Activity acknowledgment monitoring system. Utilizing off-the-rack sensors, we executed a movement location system for identifying five sorts of action: falling, lying down, sitting, standing, and walking. The framework collects and analyzes sensory data in real-time, and provides different feedback to the users. In addition, it can generate alerts based on the detected events and store the data collected to a medical server.

Keywords: ADL, SVM, TRIL , MEMS

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Authors: Laicheng Cao, Xiangqian Su, Youxiao Wu

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Feature selection is one of the important problems in network security, pattern recognition, data mining and other fields. In order to remove redundant features, effectively improve the detection speed of intrusion detection system, proposes a new feature selection method, which is based on the invasive weed optimization (IWO) algorithm and tabu search algorithm(TS). Use IWO as a global search, tabu search algorithm for local search, to improve the results of IWO algorithm. The experimental results show that the feature selection method can effectively remove the redundant features of network data information in feature selection, reduction time, and to guarantee accurate detection rate, effectively improve the speed of detection system.

Keywords: intrusion detection, feature selection, iwo, tabu search

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Authors: Usiobaifo Agharese Rosemary, Osaseri Roseline Oghogho

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Diabetes is a complex group of disease with a variety of causes; it is a disorder of the body metabolism in the digestion of carbohydrates food. The application of machine learning in the field of medical diagnosis has been the focus of many researchers and the use of recognition and classification model as a decision support tools has help the medical expert in diagnosis of diseases. Considering the large volume of medical data which require special techniques, experience, and high diagnostic skill in the diagnosis of diseases, the application of an artificial intelligent system to assist medical personnel in order to enhance their efficiency and accuracy in diagnosis will be an invaluable tool. In this study will propose a diabetes diagnosis model using rough set and K-nearest Neighbor classifier algorithm. The system consists of two modules: the feature extraction module and predictor module, rough data set is used to preprocess the attributes while K-nearest neighbor classifier is used to classify the given data. The dataset used for this model was taken for University of Benin Teaching Hospital (UBTH) database. Half of the data was used in the training while the other half was used in testing the system. The proposed model was able to achieve over 80% accuracy.

Keywords: classifier algorithm, diabetes, diagnostic model, machine learning

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Authors: Opeyemi Fadipe, Masud Salimian

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The COVID-19 pandemic has introduced a new normal into the world; online teaching is now the most used method of teaching over the face to face meeting. With the emergency of these teaching, online-teaching has been improved over time and with more technological advancement tools introduced. Practical courses are more demanding to teach because it requires the physical presence of the student as well as a demonstration of the equipment. In this study, a case of Lagos State University thermofluid practical was the understudy. A survey was done and give to a sample of students to fill. The result showed that the blend-approach is better for practical course teaching. Software simulation of the equipment used to conduct practical should be encouraged in the future.

Keywords: COVID-19, online teaching, t-distribution, thermofluid

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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: Mohammad Alhajeri

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This paper reports a computational fluid dynamics (CFD) investigation of cylindrical filter. Silicon carbide cylindrical filter elements have proven to be an effective mean of removing particulates to levels exceeding the new source performance standard. The CFD code is used here to understand the deposition process and the factors that affect the particles distribution over the filter element surface. Different approach cross flow velocity to filter face velocity ratios and different face velocities (ranging from 2 to 5 cm/s) are used in this study. Particles in the diameter range 1 to 100 microns are tracked through the domain. The radius of convergence (or the critical trajectory) is compared and plotted as a function of many parameters.

Keywords: filtration, CFD, CCF, hot gas filtration

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Authors: Rachel Byrne

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Background: Due to patient and student safety concerns, combined with clinical teachers being redeployed to clinical practice, COVID-19 has resulted in a reduction in face-to-face teaching sessions for medical students. Traditionally such sessions are particularly important for final year medical students, especially in preparing for their final practical exams. A reduced student presence on the wards has also resulted in fewer opportunities for junior doctors to provide teaching sessions. This has implications for junior doctors achieving their own curriculum outcomes for teaching, as well as potentially hindering the development of a future interest in medical education. Aims: The aims of the study are 1) To create a safe face-to-face teaching environment during COVID-19 which focussed on exam preparation for final year medical students, 2) To provide a platform for doctors to gain teaching experience, 3 ) to enable doctors to gain feedback or assessments on their teaching, 4) To create beginners guide to designing a new teaching program for future junior doctors. Methods: We created a program of timed clinical stations consisting of four sessions every five weeks during the student’s medicine attachment. Each session could be attended by 6 students and consisted of 6 stations ran by junior doctors, with each station following social distancing and personal protective equipment requirements. Junior doctors were asked to design their own stations. The sessions ran out-of-hours on weekday evenings and were optional for the students. Results: 95/95 students and 20/40 doctors involved in the programme completed feedback. 100% (n=95) of students strongly agreed/agreed that sessions were aimed at an appropriate level and provided constructive feedback. 100% (n=95) of students stated they felt more confident in their abilities and would recommend the session to peers. 90% (n=18) of the teachers strongly agreed/agreed that they felt more confident in their teaching abilities and that the sessions had improved their own medical knowledge. 85% (n=17) of doctors had a teaching assessment completed, and 83% (n=16) said the program had made them consider a career in medical education. The difficulties of creating such a program were highlighted throughout, and a beginner’s guide was created with the hopes of helping future doctors who are interested in teaching address the common obstacles.

Keywords: COVID-19, education, safety, medical

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Authors: Sheel Chakraborty

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Modern society in developed countries has distanced itself from the earlier norm of joint family living, and with the increase of economic pressure, parents' availability for their children during their infant years has been consistently decreasing over the past three decades. During the same time, the pre-primary education system - built mainly on the developmental psychology theory framework of Jean Piaget and Lev Vygotsky, has been promoted in the US through the legislature and funding. Early care and education may have a positive impact on young minds, but a growing number of kids facing social challenges in making friendships in their teenage years raises serious concerns about its effectiveness. The survey-based primary research presented here shows a statistically significant number of millennials between the ages of 10 and 25 prefer to build friendships virtually than face-to-face interactions. Moreover, many teenagers depend more on their virtual friends whom they never met. Contrary to the belief that early social interactions in a non-home setup make the kids confident and more prepared for the real world, many shy-natured kids seem to develop a sense of shakiness in forming social relationships, resulting in loneliness by the time they are young adults. Reflecting on George Mead’s theory of self that is made up of “I” and “Me”, most functioning homes provide the required freedom and forgivable, congenial environment for building the "I" of a toddler; however, daycare or preschools can barely match that. It seems social images created from the expectations perceived by preschoolers “Me" in a non-home setting may interfere and greatly overpower the formation of a confident "I" thus creating a crisis around the inability to form friendships face to face when they grow older. Though the pervasive nature of social media can’t be ignored, the non-parental early care and education practices adopted largely by the urban population have created a favorable platform of teen psychology on which social media popularity thrived, especially providing refuge to shy Gen-Z teenagers. This can explain why young adults today perceive social media as their preferred outlet of expression and a place to form dependable friendships, despite the risk of being cyberbullied.

Keywords: digital socialization, shyness, developmental psychology, friendship, early education

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Authors: Khoi A. Nguyen, Rodney A. Stewart, Hong Zhang

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‘Water-related energy’ is energy use which is directly or indirectly influenced by changes to water use. Informatics applying a range of mathematical, statistical and rule-based approaches can be used to reveal important information on demand from the available data provided at second, minute or hourly intervals. This study aims to combine these two concepts to improve the current water end use disaggregation problem through applying a wide range of most advanced pattern recognition techniques to analyse the concurrent high-resolution water-energy consumption data. The obtained results have shown that recognition accuracies of all end-uses have significantly increased, especially for mechanised categories, including clothes washer, dishwasher and evaporative air cooler where over 95% of events were correctly classified.

Keywords: deep learning network, smart metering, water end use, water-energy data

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Authors: J. K. Adedeji, S. T. Ijatuyi

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The application of neural network using pattern recognition to study the fluid dynamics and predict the groundwater reservoirs properties has been used in this research. The essential of geophysical survey using the manual methods has failed in basement environment, hence the need for an intelligent computing such as predicted from neural network is inevitable. A non-linear neural network with an XOR (exclusive OR) output of 8-bits configuration has been used in this research to predict the nature of groundwater reservoirs and fluid dynamics of a typical basement crystalline rock. The control variables are the apparent resistivity of weathered layer (p₁), fractured layer (p₂), and the depth (h), while the dependent variable is the flow parameter (F=λ). The algorithm that was used in training the neural network is the back-propagation coded in C++ language with 300 epoch runs. The neural network was very intelligent to map out the flow channels and detect how they behave to form viable storage within the strata. The neural network model showed that an important variable g_r (gravitational resistance) can be deduced from the elevation and apparent resistivity p_a. The model results from SPSS showed that the coefficients, a, b and c are statistically significant with reduced standard error at 5%.

Keywords: gravitational resistance, neural network, non-linear, pattern recognition

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Authors: Naghmeh Moradpoor Sheykhkanloo

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Structured Query Language Injection (SQLI) attack is a code injection technique in which malicious SQL statements are inserted into a given SQL database by simply using a web browser. Losing data, disclosing confidential information or even changing the value of data are the severe damages that SQLI attack can cause on a given database. SQLI attack has also been rated as the number-one attack among top ten web application threats on Open Web Application Security Project (OWASP). OWASP is an open community dedicated to enabling organisations to consider, develop, obtain, function, and preserve applications that can be trusted. In this paper, we propose an effective pattern recognition neural network model for detection and classification of SQLI attacks. The proposed model is built from three main elements of: a Uniform Resource Locator (URL) generator in order to generate thousands of malicious and benign URLs, a URL classifier in order to: 1) classify each generated URL to either a benign URL or a malicious URL and 2) classify the malicious URLs into different SQLI attack categories, and an NN model in order to: 1) detect either a given URL is a malicious URL or a benign URL and 2) identify the type of SQLI attack for each malicious URL. The model is first trained and then evaluated by employing thousands of benign and malicious URLs. The results of the experiments are presented in order to demonstrate the effectiveness of the proposed approach.

Keywords: neural networks, pattern recognition, SQL injection attacks, SQL injection attack classification, SQL injection attack detection

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Authors: Sugandhi, Parteek Kumar, Sanmeet Kaur

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Sign Language (SL) is used by deaf and other people who cannot speak but can hear or have a problem with spoken languages due to some disability. It is a visual gesture language that makes use of either one hand or both hands, arms, face, body to convey meanings and thoughts. SL automation system is an effective way which provides an interface to communicate with normal people using a computer. In this paper, an avatar based dictionary has been proposed for text to Indian Sign Language (ISL) generation system. This research work will also depict a literature review on SL corpus available for various SL s over the years. For ISL generation system, a written form of SL is required and there are certain techniques available for writing the SL. The system uses Hamburg sign language Notation System (HamNoSys) and Signing Gesture Mark-up Language (SiGML) for ISL generation. It is developed in PHP using Web Graphics Library (WebGL) technology for 3D avatar animation. A multilingual ISL dictionary is developed using HamNoSys for both English and Hindi Language. This dictionary will be used as a database to associate signs with words or phrases of a spoken language. It provides an interface for admin panel to manage the dictionary, i.e., modification, addition, or deletion of a word. Through this interface, HamNoSys can be developed and stored in a database and these notations can be converted into its corresponding SiGML file manually. The system takes natural language input sentence in English and Hindi language and generate 3D sign animation using an avatar. SL generation systems have potential applications in many domains such as healthcare sector, media, educational institutes, commercial sectors, transportation services etc. This research work will help the researchers to understand various techniques used for writing SL and generation of Sign Language systems.

Keywords: avatar, dictionary, HamNoSys, hearing impaired, Indian sign language (ISL), sign language

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Authors: Fehmeen Anwar, Ujala Liaqat

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For over half a century, U.S. and the Soviet Union have been at daggers drawn with each other. Both leading powers of the world have been struggling hard to surpass each other in military and other technological fields. This neck-to-neck competition turned in favour of U.S. in the early 1990s when USSR had to face economic stagnation and later dismemberment of several of its states. The predominance of U.S. is still evident to date, rather it continues to grow. With this proposed defence program i.e. Anti-Ballistic Missile System, the U.S. will have a considerable chance of intercepting any nuclear strike by Russia, which re-asserts U.S. dominance in the region and creating a security dilemma for Russia and other states. The question is whether America’s recent nuclear deterrence project is merely to counter nuclear threats from Iran and North Korea or is it purely directed towards Russia, thus ensuring complete military supremacy in the world. Although U.S professes to direct its Anti-Ballistic Missile System (ABMS) against the axis of evil (Iran and North Korea), yet the deployment of this system in the East European territory undermines the Russian nuclear strategic capability, as this enables U.S. to initiate an attack and guard itself from retaliatory strike, thus disturbing the security equilibrium in Europe. The implications of this program can lead to power imbalance which can lead to the emergence of fundamentally different paradigm of international politics.

Keywords: Anti-Ballistic Missile System (ABMS), cold-war, axis of evil, power dynamics

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Authors: Martha Serrano-Arias, Martha Frías-Armenta

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Significant results have been found both supporting universality of emotion recognition and cultural background influence. Thus, the aim of this research was to test a Mexican version of the MTSI in different cultures to find differences in their performance. The MTSI-Mx assesses through a scenario approach were subjects must evaluate real persons. Two target persons were used for the construction, a man (FS) and a woman (AD). The items were grouped in four variables: Picture, Video, and FS and AD scenarios. The test was applied to 201 students from Mexico and Germany. T-test for picture and FS scenario show no significance. Video and AD had a significance at the 5% level. Results show slight differences between cultures, although a more comprehensive research is needed to conclude which culture can perform better in this kind of assessments.

Keywords: emotion recognition, MTSI, social intelligence, transcultural study

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Authors: K. Durairaj, I. N. Umar

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The asynchronous discussion forum is one of the most widely used activities in learning management system environment. Online forum allows participants to interact, construct knowledge, and can be used to complement face to face sessions in blended learning courses. However, to what extent do the students perceive the benefits or advantages of forum remain to be seen. Through content and social network analyses, instructors will be able to gauge the students’ engagement and knowledge construction level. Thus, this study aims to analyze the students’ level of knowledge construction and their participation level that occur through online discussion. It also attempts to investigate the relationship between the level of knowledge construction and their social interaction patterns. The sample involves 23 students undertaking a master course in one public university in Malaysia. The asynchronous discussion forum was conducted for three weeks as part of the course requirement. The finding indicates that the level of knowledge construction is quite low. Also, the density value of 0.11 indicating that the overall communication among the participants in the forum is low. This study reveals that strong and significant correlations between SNA measures (in-degree centrality, out-degree centrality) and level of knowledge construction. Thus, allocating these active students in a different groups aids the interactive discussion takes place. Finally, based upon the findings, some recommendations to increase students’ level of knowledge construction and also for further research are proposed.

Keywords: asynchronous discussion forums, content analysis, knowledge construction, social network analysis

Procedia PDF Downloads 362