Search results for: psychology for language learning

Authors: Abdullah Alotaiq, David Wallom, Malcolm McCulloch

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This study presents an archetype-based approach to designing effective strategies for low-carbon transitions in the power sector. To achieve global energy transition goals, a renewable energy transition is critical, and understanding diverse energy landscapes across different countries is essential to design effective renewable energy policies and strategies. Using a clustering approach, this study identifies 12 energy archetypes based on the electricity mix, socio-economic indicators, and renewable energy contribution potential of 187 UN countries. Each archetype is characterized by distinct challenges and opportunities, ranging from high dependence on fossil fuels to low electricity access, low economic growth, and insufficient contribution potential of renewables. Archetype A, for instance, consists of countries with low electricity access, high poverty rates, and limited power infrastructure, while Archetype J comprises developed countries with high electricity demand and installed renewables. The study findings have significant implications for renewable energy policymaking and investment decisions, with policymakers and investors able to use the archetype approach to identify suitable renewable energy policies and measures and assess renewable energy potential and risks. Overall, the archetype approach provides a comprehensive framework for understanding diverse energy landscapes and accelerating decarbonisation of the power sector.

Keywords: fossil fuels, power plants, energy transition, renewable energy, archetypes

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Authors: Asma Rebaya, Kaouther Gasmi, Imen Amari, Salem Hasnaoui

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Over these last years, applications such as telecommunications, signal processing, digital communication with advanced features (Multi-antenna, equalization..) witness a rapid evaluation accompanied with an increase of user exigencies in terms of latency, the power of computation… To satisfy these requirements, the use of hardware/software systems is a common solution; where hardware is composed of multi-cores and software is represented by models of computation, synchronous data flow (SDF) graph for instance. Otherwise, the most of the embedded system designers utilize Simulink for modeling. The issue is how to simplify the c code generation, for a multi-cores platform, of an application modeled by Simulink. To overcome this problem, we propose a workflow allowing an automatic transformation from the Simulink model to the SDF graph and providing an efficient schedule permitting to optimize the number of cores and to minimize latency. This workflow goes from a Simulink application and a hardware architecture described by IP.XACT language. Based on the synchronous and hierarchical behavior of both models, the Simulink block diagram is automatically transformed into an SDF graph. Once this process is successfully achieved, the scheduler calculates the optimal cores’ number needful by minimizing the maximum density of the whole application. Then, a core is chosen to execute a specific graph task in a specific order and, subsequently, a compatible C code is generated. In order to perform this proposal, we extend Preesm, a rapid prototyping tool, to take the Simulink model as entry input and to support the optimal schedule. Afterward, we compared our results to this tool results, using a simple illustrative application. The comparison shows that our results strictly dominate the Preesm results in terms of number of cores and latency. In fact, if Preesm needs m processors and latency L, our workflow need processors and latency L'< L.

Keywords: hardware/software system, latency, modeling, multi-cores platform, scheduler, SDF graph, Simulink model, workflow

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Authors: Hilal Naimi, Amelbahahouda Adamou-Mitiche, Lahcene Mitiche

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The main problem in the area of medical imaging has been image denoising. The most defying for image denoising is to secure data carrying structures like surfaces and edges in order to achieve good visual quality. Different algorithms with different denoising performances have been proposed in previous decades. More recently, models focused on deep learning have shown a great promise to outperform all traditional approaches. However, these techniques are limited to the necessity of large sample size training and high computational costs. This research proposes a denoising approach basing on LDTCWT (Lifting Dual Tree Complex Wavelet Transform) using Hybrid Thresholding with Wiener filter to enhance the quality image. This research describes the LDTCWT as a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). To develop this approach, a hybrid thresholding function is modeled by integrating the Wiener filter into the thresholding function.

Keywords: lifting wavelet transform, image denoising, dual tree complex wavelet transform, wavelet shrinkage, wiener filter

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Authors: Rafat Alwazna

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Translation teaching is a particular activity that includes translators and interpreters training either inside or outside institutionalised settings, such as universities. It can also serve as a means of teaching other fields, such as foreign languages. Translation teaching began in the twentieth century. Teachers of translation hold the responsibilities of educating students, developing their translation competence and training them to be professional translators. The activity of translation teaching involves various tasks, including curriculum design, course delivery, material writing as well as application and implementation. The present paper addresses translation teaching during COVID-19 outbreak, seeking to find out the challenges encountered by translation teachers in online translation teaching and the discoveries/solutions arrived at to resolve them. The paper makes use of a comprehensive questionnaire, containing closed-ended and open-ended questions to elicit both quantitative as well as qualitative data from about sixty translation teachers who have been teaching translation at BA and MA levels during COVID-19 outbreak. The data shows that about 40% of the participants evaluate their online translation teaching experience during COVID-19 outbreak as enjoyable and exhilarating. On the contrary, no participant has evaluated his/her online translation teaching experience as being not good, nor has any participant evaluated his/her online translation teaching experience as being terrible. The data also presents that about 23.33% of the participants evaluate their online translation teaching experience as very good, and the same percentage applies to those who evaluate their online translation teaching experience as good to some extent. Moreover, the data indicates that around 13.33% of the participants evaluate their online translation teaching experience as good. The data also demonstrates that the majority of the participants have encountered obstacles in online translation teaching and have concurrently proposed solutions to resolve them.

Keywords: online translation teaching, electronic learning platform, COVID-19 outbreak, challenges, solutions

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Authors: Rashida Suleiman, Asamoah Jnr. Boakye, Suleiman Ahmed Ibn Ahmed

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In recent years, there has been an enormous success in discoveries of scientific knowledge in medicine coupled with the advancement of technology. Despite all these successes, diagnoses and treatment of diseases have become complex. MYCIN is a groundbreaking illustration of a clinical decision support system (CDSS), which was developed to assist physicians in the diagnosis and treatment of bacterial infections by providing suggestions for antibiotic regimens. MYCIN was one of the earliest expert systems to demonstrate how CDSSs may assist human decision-making in complicated areas. Relevant databases were searched using google scholar, PubMed and general Google search, which were peculiar to clinical decision support systems. The articles were then screened for a comprehensive overview of the functionality, consultative style and statistical usage of MYCIN, a clinical decision support system. Inferences drawn from the articles showed some usage of MYCIN for problem-based learning among clinicians and students in some countries. Furthermore, the data demonstrated that MYCIN had completed clinical testing at Stanford University Hospital following years of research. The system (MYCIN) was shown to be extremely accurate and effective in diagnosing and treating bacterial infections, and it demonstrated how CDSSs might enhance clinical decision-making in difficult circumstances. Despite the challenges MYCIN presents, the benefits of its usage to clinicians, students and software developers are enormous.

Keywords: clinical decision support system, MYCIN, diagnosis, bacterial infections, support systems

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Authors: Ru-Yin Yang, Chi Wu, Cheng-Yu Tsai, Yin-Tzu Lin, Wen-Te Liu

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Background: Sleep breathing disorder (SDB) is a condition demonstrated by recurrent episodes of the airway obstruction leading to intermittent hypoxia and quality fragmentation during sleep time. However, the procedures for SDB severity examination remain complicated and costly. Objective: The objective of this study is to establish a simplified examination method for SDB by the respiratory impendence pattern sensor combining the signal processing and machine learning model. Methodologies: We records heart rate variability by the electrocardiogram and respiratory pattern by impendence. After the polysomnography (PSG) been done with the diagnosis of SDB by the apnea and hypopnea index (AHI), we calculate the episodes with the absence of flow and arousal index (AI) from device record. Subjects were divided into training and testing groups. Neuron network was used to establish a prediction model to classify the severity of the SDB by the AI, episodes, and body profiles. The performance was evaluated by classification in the testing group compared with PSG. Results: In this study, we enrolled 66 subjects (Male/Female: 37/29; Age:49.9±13.2) with the diagnosis of SDB in a sleep center in Taipei city, Taiwan, from 2015 to 2016. The accuracy from the confusion matrix on the test group by NN is 71.94 %. Conclusion: Based on the models, we established a prediction model for SDB by means of the wearable sensor. With more cases incoming and training, this system may be used to rapidly and automatically screen the risk of SDB in the future.

Keywords: sleep breathing disorder, apnea and hypopnea index, body parameters, neuron network

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Authors: N. Nor Azman, M. F. Kamarudin, S. I. Ong, N. Maaulot

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PERMATApintar^® National Gifted Center is, to the author’s best of knowledge, the first center in Malaysia that provides a platform for Malaysian talented students with high ability in thinking. This center has built a teaching and learning biology curriculum that suits the ability of these gifted students. The level of PERMATApintar^® biology curriculum is basically higher than the national biology curriculum. Here, the foundation students are exposed to the PERMATApintar^® biology curriculum at the age of as early as 11 years old. This center practices a 4-time-a-year examination system to monitor the academic performances of the students. Generally, most of the time, male students show no or low interest towards biology subject compared to female students. This study is to investigate the association of students’ gender and their academic performances in biology examination. A total of 39 students’ scores in twelve sets of biology examinations in 3 years have been collected and analyzed by using the statistical analysis. Based on the analysis, there are no significant differences between male and female students against the biology academic performances with a significant level of p = 0.05. This indicates that gender is not associated with the scores of biology examinations among the students. Another result showed that the average score for male studenta was higher than the female students. Future research can be done by comparing the biology academic achievement in Malaysian National Examination (Sijil Pelajaran Malaysia, SPM) between the Foundation 3 students (Grade 9) and Level 2 students (Grade 11) with similar PERMATApintar^® biology curriculum.

Keywords: academic performances, biology, gender differences, gifted students,

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Authors: Sonia David, Uma Warrier

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Background: Film therapy is the use of commercial or non-commercial films to enhance healing for therapeutic purposes. Objectives: The mixed-method study aims to evaluate the effect of film-based counseling on body image dissatisfaction among adolescents to precisely ascertain the cause of the alteration in body image dissatisfaction due to the said intervention. Method: The one group pre-test post-test research design study using inferential statistics and thematic analysis is based on a pre-test post-test design conducted on 44 school-going adolescents between 13 and 17. The Body Shape Questionnaire (BSQ- 34) was used as a pre-test and post-test measure. The film-based counseling intervention model was used through individual counseling sessions. The analysis involved paired sample t-test used to examine the data quantitatively, and thematic analysis was used to evaluate qualitative data. Findings: The results indicated that there is a significant difference between the pre-test and post-test means. Since t(44)= 9.042 is significant at a 99% confidence level, it is ascertained that film-based counseling intervention reduces body image dissatisfaction. The five distinct themes from the thematic analysis are “acceptance, awareness, empowered to change, empathy, and reflective.” Novelty: The paper originally contributes to the repertoire of research on film therapy as a successful counseling intervention for addressing the challenges of body image dissatisfaction. This study also opens avenues for considering alteration of teaching pedagogy to include video-based learning in various subjects.

Keywords: body image dissatisfaction, adolescents, film-based counselling, film therapy, acceptance and commitment therapy

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Authors: Anisa Suraya Ab Razak, Izza Hayat

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Introduction: The diagnosis and management of severe hyponatremia in neuropsychiatric patients present a significant challenge to physicians. Several factors contribute, including diagnostic shadowing and attributing abnormal behavior to intellectual disability or psychiatric conditions. Hyponatraemia is the commonest electrolyte abnormality in the inpatient population, ranging from mild/asymptomatic, moderate to severe levels with life-threatening symptoms such as seizures, coma and death. There are several documented fatal case reports in the literature of severe hyponatremia secondary to psychogenic polydipsia, often diagnosed only in autopsy. This paper presents a case study of acute severe hyponatremia in a neuropsychiatric patient with early diagnosis and admission to intensive care. Case study: A 21-year old Caucasian male with known epilepsy and learning disability was admitted from residential living with generalized tonic-clonic self-terminating seizures after refusing medications for several weeks. Evidence of superficial head injury was detected on physical examination. His laboratory data demonstrated mild hyponatremia (125 mmol/L). Computed tomography imaging of his brain demonstrated no acute bleed or space-occupying lesion. He exhibited abnormal behavior - restlessness, drinking water from bathroom taps, inability to engage, paranoia, and hypersexuality. No collateral history was available to establish his baseline behavior. He was loaded with intravenous sodium valproate and leveritircaetam. Three hours later, he developed vomiting and a generalized tonic-clonic seizure lasting forty seconds. He remained drowsy for several hours and regained minimal recovery of consciousness. A repeat set of blood tests demonstrated profound hyponatremia (117 mmol/L). Outcomes: He was referred to intensive care for peripheral intravenous infusion of 2.7% sodium chloride solution with two-hourly laboratory monitoring of sodium concentration. Laboratory monitoring identified dangerously rapid correction of serum sodium concentration, and hypertonic saline was switched to a 5% dextrose solution to reduce the risk of acute large-volume fluid shifts from the cerebral intracellular compartment to the extracellular compartment. He underwent urethral catheterization and produced 8 liters of urine over 24 hours. Serum sodium concentration remained stable after 24 hours of correction fluids. His GCS recovered to baseline after 48 hours with improvement in behavior -he engaged with healthcare professionals, understood the importance of taking medications, admitted to illicit drug use and drinking massive amounts of water. He was transferred from high-dependency care to ward level and was initiated on multiple trials of anti-epileptics before achieving seizure-free days two weeks after resolution of acute hyponatremia. Conclusion: Psychogenic polydipsia is often found in young patients with intellectual disability or psychiatric disorders. Patients drink large volumes of water daily ranging from ten to forty liters, resulting in acute severe hyponatremia with mortality rates as high as 20%. Poor outcomes are due to challenges faced by physicians in making an early diagnosis and treating acute hyponatremia safely. A low index of suspicion of water intoxication is required in this population, including patients with known epilepsy. Monitoring urine output proved to be clinically effective in aiding diagnosis. Early referral and admission to intensive care should be considered for safe correction of sodium concentration while minimizing risk of fatal complications e.g. central pontine myelinolysis.

Keywords: epilepsy, psychogenic polydipsia, seizure, severe hyponatremia

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Authors: Mona Salah El-Din Hassanein

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Faced with a sudden, unexpected, and overwhelming event, the individual's normal cognitive processing may cease to function, trapping the psyche in "speechless terror", while images, feelings and sensations are experienced with emotional intensity. Unable to master such situation, the individual becomes a trauma victim who will be susceptible to traumatic recollections like intrusive thoughts, flashbacks, and repetitive re-living of the primal event in a way that blurs the distinction between past and present, and forecloses the future. Trauma is timeless, repetitious, and contagious; a trauma observer could fall prey to "secondary victimhood". Central to the process of healing the psychic wounds in the aftermath of trauma is verbalizing the traumatic experience (i.e., putting it into words) – an act which provides a chance for assimilation, testimony, and reevaluation. In light of this paradigm, this paper proposes a reading of Oscar Wilde's The Ballad of Reading Gaol, written shortly after his release from prison, as a post-traumatic text which traces the disruptive effects of the traumatic experience of Wilde's imprisonment for homosexual offences and the ensuing reversal of fortune he endured. Post-traumatic writing demonstrates the process of "working through" a trauma which may lead to the possibility of ethical agency in the form of a "survivor mission". This paper draws on fundamental concepts and key insights in literary trauma theory which is characterized by interdisciplinarity, combining the perspectives of different fields like critical theory, psychology, psychiatry, psychoanalysis, history, and social studies. Of particular relevance to this paper are the concepts of "vicarious traumatization" and "survivor mission", as The Ballad of Reading Gaol was written in response to Wilde's own prison trauma and the indirect traumatization he experienced as a result of witnessing the execution of a fellow prisoner whose story forms the narrative base of the poem. The Ballad displays Wilde's sense of mission which leads him to recognize the social as well as ethical implications of personal tragedy. Through a close textual analysis of The Ballad of Reading Gaol within the framework of literary trauma theory, the paper aims to: (a) demonstrate how the poem's thematic concerns, structure and rhetorical figures reflect the structure of trauma; (b) highlight Wilde's attempts to come to terms with the effects of the cataclysmic experience which transformed him into a social outcast; and (c) show how Wilde manages to transcend the victim status and assumes the role of ethical agent to voice a critique of the Victorian penal system and the standards of morality underlying the cruelties practiced against wrong doers and to solicit social action.

Keywords: ballad of reading of reading, post-traumatic writing, trauma theory, Wilde

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Authors: Bolajoko I. Malomo, Maryam O. Yusuf

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Lagos urban mini-bus drivers play a critical role in the transportation sector. The current major mode of transportation within Lagos metropolis remains road transportation and this confirms the relevance of urban mini-bus drivers in transporting the populace to their various destinations. Other modes of transportation such as the train and waterways are currently inadequate. Various threats to the well-being of urban bus drivers include congested traffic typical of modern day lifestyles, dwindling financial returns due to long hours in traffic, fewer hours of sleep, inadequate diet, time pressure, and assaults related to fare disputes. Several health-related problems have been documented to be associated with urban bus driving. For instance, greater rates of hypertension, obesity and cholesterol level has been reported. Research studies are yet to identify the influence of age and marital status on the well-being of urban mini-bus drivers in Lagos metropolis. A study of this nature is necessary as it is culturally perceived in Nigeria that older and married people are especially influenced by family affiliation and would behave in ways that would project positive outcomes. The study sample consisted of 150 urban mini-bus drivers who were conveniently sampled from six (6) different terminuses where their journey begins and terminates. The well-being questionnaire was administered to participants. The criteria for inclusion in the study included the ability to read in English language and the confirmation that interested participants were on duty and suited to be driving mini-buses. Due to the nature of the job of bus driving, the researcher administered the questionnaires on participants who were free and willing to respond to the survey. All participants were males of various age groups and of different marital statuses. Results of analyses conducted revealed no significant influence of age and marital status on the well-being of urban mini-bus drivers. This indicates that the well-being of urban mini-bus drivers is not influenced by age nor marital status. The findings of this study have cultural implications. It negates the popularly held belief that older and married people care more about their well-being than younger and single people. It brings to fore the need to also identify and consider other factors when certifying people for the job of urban bus driving.

Keywords: age, Lagos metropolis, marital status, well-being of urban mini bus drivers

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Authors: Safak Akdemir

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The main purpose of this research is to evaluate the relationships between childhood traumas and dissociative experiences, self-perceptions and early maladaptive schemas in male convicts convicted of sexual assault crimes in prison. In our study, male convicts in prison for the crime of sexual assault constitute the experimental group, and the participants matched with this experimental group in terms of education, age and gender constitute the control group. The experimental group of the research consists of 189 male individuals who are convicted in the Ministry of Justice, General Directorate of Prisons, Istanbul/Maltepe L Type Closed Prison. The control group of this study consists of 147 adult males matched with the experimental group in terms of age, gender and education parameters. A total of 336 adult male individuals are included in the sample of this study. 46% of the experimental group were convicted of only sexual assault, 54% of them were convicted of both sexual assault and murder, injury and drug crimes. Total of five data collection tools, namely the Personal Information Form created by S. A. & E. O., Childhood Trauma Questionnaire (CTQ), the Dissociative Experiences Scale (DES), the Rosenberg Self-Esteem Scale (RSES), and the Young Schema Questionnaire-Short Form (YSQ-SF3), were completed. DES cut-off score of 99 (52.39%) of 189 convicts in the experimental group and 12 (8.17%) of 147 people in the control group was found to be 30 and above, and this result indicates the presence of pathological dissociative experiences. 180 (95.23%) of the sexual assault convicts in the experimental group had at least one childhood trauma, 154 (81.48%) were emotional neglect, 140 (74.07%) were emotional abuse, 121 (64.02%) were physical neglect, 91 (4814%) physical abuse and 70 (37.03%) sexual abuse. 168 (88.88%) of the experimental group reported multiple type of trauma and 12 (6.34%) reported single type of trauma. While the childhood traumas, isolation, abandonment and emotional deprivation schema levels of the convicts with a DES cut-off score of 30 and above are higher than the convicts with a DES cut-off score of 30 and above, their self-esteem is lower than this group. Experimental group while childhood traumas, dissociative experiences and early maladaptive schemas are higher than the control group, their self-esteem levels are lower. Dissociative experiences, abandonment and emotional deprivation early maladaptive schemas are more common in convicts aged between 18-30 years compared to convicts aged 31 and over. In addition, dissociative experiences and early maladaptive schemas of male convicts who reported physical and sexual abuse were higher than those who did not report physical and sexual abuse, while their self-esteem was at a lower level. As a result, in terms of psychotraumatology and clinical forensic psychology, dissociative disorders developed under the influence of chronic childhood traumas, with clinical interviews and psychometric measurements to be made in terms of forensic psychiatry; it is of fundamental importance to evaluate it in terms of neurosis-psychosis distinction, disability retirement, custody, malpractice, criminal and legal capacity criteria.

Keywords: crime, sexual assault, criminology, rape crimes, dissocitative disorders, maladative schemas

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Authors: Jamunarani Appalasamy, Tha Kyi Kyi, Quek Kia Fatt, Joyce Pauline Joseph, Anuar Zaini M. Zain

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Background: The Health Belief Theory had always been associated with chronic disease management. Various health behaviour concepts and perception branching from this Health Belief Theory had involved with medication understanding, use, and self-efficacy which directly link to medication adherence. In a previous quantitative and qualitative study, stroke patients in Malaysia were found to be strongly believing information obtained by various sources such as the internet and social communication. This action leads to lower perception of their stroke preventative medication benefit which in long-term creates non-adherence. Hence, this study intends to pilot an intervention which uses audio-visual concept incorporated with mHealth service to enhance learning and self-reflection among stroke patients to manage their disease. Methods/Design: Twenty patients will be allocated to a proposed intervention whereas another twenty patients are allocated to the usual treatment. The intervention involves a series of developed audio-visual videos sent via mobile phone which later await for responses and feedback from the receiver (patient) via SMS or recorded calls. The primary outcome would be the medication understanding, use and self-efficacy measured over two months pre and post intervention. Secondary outcome is measured from changes of blood parameters and other self-reported questionnaires. Discussion: This study shall also assess uptake/attrition, feasibility, and acceptability of this intervention. Trial Registration: NMRR-15-851-24737 (IIR)

Keywords: health belief, medication understanding, medication use, self-efficacy

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Authors: Zhu Weimin, Allegorico Carmine, Ruggiero Gionata

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During decades of Baker Hughes (BH) iCenter experience, it is demonstrated that in addition to conventional insights on equipment steady operation conditions, insights on transient conditions can add significant and exclusive value for anomaly detection, downtime saving, and predictive maintenance. Our work shows examples from the BH iCenter experience to introduce the advantages and features of using transient condition analytics: (i) Operation under critical engine conditions: e.g., high level or high change rate of temperature, pressure, flow, vibration, etc., that would not be reachable in normal operation, (ii) Management of dedicated sub-systems or components, many of which are often bottlenecks for reliability and maintenance, (iii) Indirect detection of anomalies in the absence of instrumentation, (iv) Repetitive sequences: if data is properly processed, the engineering features of transients provide not only anomaly detection but also problem characterization and prognostic indicators for predictive maintenance, (v) Engine variables accounting for fatigue analysis. iCenter has been developing and deploying a series of analytics based on transient conditions. They are contributing to exclusive value adding in the following areas: (i) Reliability improvement, (ii) Startup reliability improvement, (iii) Predictive maintenance, (iv) Repair/overhaul cost down. Illustrative examples for each of the above areas are presented in our study, focusing on challenges and adopted techniques ranging from purely statistical approaches to the implementation of machine learning algorithms. The obtained results demonstrate how the value is obtained using transient condition analytics in the BH iCenter experience.

Keywords: analytics, diagnostics, monitoring, turbomachinery

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Authors: Yasamin Rahimi, Ali Kamandi, Abbas Hoseini, Hesam Haddad

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Online/offline chat is a convenient approach in the electronic markets of second-hand products in which potential customers would like to have more information about the products to fill the information gap between buyers and sellers. Online peer in peer market is trying to create artificial intelligence-based systems that help customers ask more informative questions in an easier way. In this article, we introduce a method for the question/answer system that we have developed for the top-ranked electronic market in Iran called Divar. When it comes to secondhand products, incomplete product information in a purchase will result in loss to the buyer. One way to balance buyer and seller information of a product is to help the buyer ask more informative questions when purchasing. Also, the short time to start and achieve the desired result of the conversation was one of our main goals, which was achieved according to A/B tests results. In this paper, we propose and evaluate a method for suggesting questions and answers in the messaging platform of the e-commerce website Divar. Creating such systems is to help users gather knowledge about the product easier and faster, All from the Divar database. We collected a dataset of around 2 million messages in Persian colloquial language, and for each category of product, we gathered 500K messages, of which only 2K were Tagged, and semi-supervised methods were used. In order to publish the proposed model to production, it is required to be fast enough to process 10 million messages daily on CPU processors. In order to reach that speed, in many subtasks, faster and simplistic models are preferred over deep neural models. The proposed method, which requires only a small amount of labeled data, is currently used in Divar production on CPU processors, and 15% of buyers and seller’s messages in conversations is directly chosen from our model output, and more than 27% of buyers have used this model suggestions in at least one daily conversation.

Keywords: smart reply, spell checker, information retrieval, intent detection, question answering

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Authors: Anastasios Papathanasiou, George Liontos, Athanasios Katsouras, Vasiliki Liagkou, Euripides Glavas

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Email remains a crucial communication tool due to its efficiency, accessibility and cost-effectiveness, enabling rapid information exchange across global networks. However, the global adoption of email has also made it a prime target for cyber threats, including phishing, malware and Business Email Compromise (BEC) attacks, which exploit its integral role in personal and professional realms in order to perform fraud and data breaches. To combat these threats, this research advocates for a multi-layered defense strategy incorporating advanced technological tools such as anti-spam and anti-malware software, machine learning algorithms and authentication protocols. Moreover, we developed an artificial intelligence model specifically designed to analyze email headers and assess their security status. This AI-driven model examines various components of email headers, such as "From" addresses, ‘Received’ paths and the integrity of SPF, DKIM and DMARC records. Upon analysis, it generates comprehensive reports that indicate whether an email is likely to be malicious or benign. This capability empowers users to identify potentially dangerous emails promptly, enhancing their ability to avoid phishing attacks, malware infections and other cyber threats.

Keywords: email security, artificial intelligence, header analysis, threat detection, phishing, DMARC, DKIM, SPF, ai model

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Authors: Kothuri Sriraman, Mattupalli Komal Teja

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In this paper, a novel algorithm is proposed for the recognition of hulls in a hand written images that might be irregular or digit or character shape. Identification of objects and internal objects is quite difficult to extract, when the structure of the image is having bulk of clusters. The estimation results are easily obtained while going through identifying the sub-regional objects by using the SASK algorithm. Focusing mainly to recognize the number of internal objects exist in a given image, so as it is shadow-free and error-free. The hard clustering and density clustering process of obtained image rough set is used to recognize the differentiated internal objects, if any. In order to find out the internal hull regions it involves three steps pre-processing, Boundary Extraction and finally, apply the Hull Detection system. By detecting the sub-regional hulls it can increase the machine learning capability in detection of characters and it can also be extend in order to get the hull recognition even in irregular shape objects like wise black holes in the space exploration with their intensities. Layered hulls are those having the structured layers inside while it is useful in the Military Services and Traffic to identify the number of vehicles or persons. This proposed SASK algorithm is helpful in making of that kind of identifying the regions and can useful in undergo for the decision process (to clear the traffic, to identify the number of persons in the opponent’s in the war).

Keywords: chain code, Hull regions, Hough transform, Hull recognition, Layered Outline Extraction, SASK algorithm

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Authors: R. Cooper, M. Kurien

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Background: Coeliac Disease affects around 1 in 100 people. Untreated, it can result in serious morbidity such as malabsorption and cancers. The only treatment is to adhere to a gluten free diet (GFD). International guidelines recommend that people with the coeliac disease receive follow-up healthcare annually to detect complications early and support their adherence to a GFD. However, there is a finite amount of healthcare in the UK, and as such, not all patients receive follow-up care as recommended by the guidelines. Furthermore, there is an increasing number of patients being diagnosed with coeliac disease. Given the potential severe morbidity that non-adherence to a GFD could result in, alongside reports that the rate of non- GFD adherence could be as high as 91%, it is imperative that action is taken. One potential solution to this would be to provide follow-up care digitally through utilising technology. This abstract reports on a rapid review undertaken to explore the existing evidence in this area. Methods: In June 2020, 11 bibliographic databases were searched to find any pertinent studies. The inclusion criteria required the study to be written in the English language and report on the use of digital healthcare interventions for people with Coeliac Disease. Results: A small amount of evidence (n=8) was found which met our inclusion criteria and pertained to the provision of CD follow-up digitally. These studies focussed either on educating and supporting patients to adhere to a GFD or providing consultation remotely with a focus on detecting complications early. These studies showed that there is potential for digital healthcare interventions to positively impact people with coeliac disease. However, it is suggested that the effectiveness of these interventions may depend on local circumstances, individual knowledge of CD and general attitudes. Conclusion: The above studies suggest that providing follow-up care digitally may offer a potential solution; however, the evidence about how this should be done and in what circumstances this will work for individuals is scarce. In the light of the COVID-19 pandemic, the introduction of digital healthcare interventions appears to be highly topical, and as such, this review may benefit from being refreshed in the future.

Keywords: coeliac disease, follow-up, gluten free diet, digital healthcare interventions

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Authors: Yousef Sharrab, Dimah Al-Fraihat, Monther Tarawneh, Aysh Alhroob, Ala’ Khalifeh, Nabil Sarhan

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This paper explores the evolution of Electrical Engineering (EE) and Computer Science (CS) education in higher learning, examining the feasibility of unifying them into Electrical Engineering and Computer Science (EECS) for the technology industry. It delves into the historical reasons for their separation and underscores the need for integration. Emerging technologies such as AI, Virtual Reality, IoT, Cloud Computing, and Cybersecurity demand an integrated EE and CS program to enhance students' understanding. The study evaluates curriculum integration models, drawing from prior research and case studies, demonstrating how integration can provide students with a comprehensive knowledge base for industry demands. Successful integration necessitates addressing administrative and pedagogical challenges. For academic institutions considering merging EE and CS programs, the paper offers guidance, advocating for a flexible curriculum encompassing foundational courses and specialized tracks in computer engineering, software engineering, bioinformatics, information systems, data science, AI, robotics, IoT, virtual reality, cybersecurity, and cloud computing. Elective courses are emphasized to keep pace with technological advancements. Implementing this integrated approach can prepare students for success in the technology industry, addressing the challenges of a technologically advanced society reliant on both EE and CS principles. Integrating EE and CS curricula is crucial for preparing students for the future.

Keywords: electrical engineering, computer science, EECS, curriculum integration of EE and CS

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Authors: Jianhong Xiang, Rui Sun, Linyu Wang

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In recent years, due to the continuous improvement of deep learning theory, Convolutional Neural Network (CNN) has played a great superior performance in the research of Hyperspectral Image (HSI) classification. Since HSI has rich spatial-spectral information, only utilizing a single dimensional or single size convolutional kernel will limit the detailed feature information received by CNN, which limits the classification accuracy of HSI. In this paper, we design a multi-scale CNN with MLP based on residual and attention modules (INRAM-3DCNN) for the HSI classification task. We propose to use multiple 3D convolutional kernels to extract the packet feature information and fully learn the spatial-spectral features of HSI while designing residual 3D convolutional branches to avoid the decline of classification accuracy due to network degradation. Secondly, we also design the 2D Inception module with a joint channel attention mechanism to quickly extract key spatial feature information at different scales of HSI and reduce the complexity of the 3D model. Due to the high parallel processing capability and nonlinear global action of the Multilayer Perceptron (MLP), we use it in combination with the previous CNN structure for the final classification process. The experimental results on two HSI datasets show that the proposed INRAM-3DCNN method has superior classification performance and can perform the classification task excellently.

Keywords: INRAM-3DCNN, residual, channel attention, hyperspectral image classification

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Authors: Teerapong Lorchitamnuay, Thirarut Worapishet

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In the present digital age, today's customers explore various channels before finalizing a purchase, with abundant options and information at their disposal. Despite this, there is a strong digital interconnectedness. With just a few mouse clicks, customers can gather comprehensive information about a product, free from the influence of a salesperson. Salespeople must embrace cutting-edge technology to truly redefine the essence of selling if they are to thrive in this digital era. The significance of customer-salesperson communication in companies is becoming increasingly evident. It prompts the inquiry of how companies can modify or reshape their sales teams' approaches to effectively respond to evolving customer preferences and effectively manage external shifts, all in pursuit of sustaining and expanding their enterprises. Research highlights that digital and intercultural skills are the latest competencies sought by customers from salespeople in today's fast-paced world prior to making purchases of products and services. This study seeks to examine the pivotal influences of these salesperson skills in achieving customer satisfaction. The research design encompasses the analysis of descriptive statistics and quantitative data through a regression model. Data were gathered from an online convenience survey involving 260 respondents who are customers of an air express service provider in Thailand and who engage with salespeople in a traditional manner. The findings underscore that intercultural skills have a substantial impact on customer satisfaction in the digital era, particularly concerning adaptability, foreign language proficiency, active listening, and empathy skills. Organizations should focus on nurturing beneficial habits among their salespeople; since it signifies this effort, it should extend beyond just the frontline but should extend to encompass backline units and high-level management, ensuring that everyone possesses the same customer-oriented skills. The conclusions drawn from this research provide valuable insights, affirming that digital and intercultural skills can empower organizations to optimize their workforce's competencies, thereby achieving customer satisfaction in the digital age.

Keywords: customer behavior, customer satisfaction, digital era, digital skill, intercultural skill

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Authors: Hui Zhang, Ye Tian, Fang Ye, Ziming Guo

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Communication signal modulation recognition technology is one of the key technologies in the field of modern information warfare. At present, communication signal automatic modulation recognition methods are mainly divided into two major categories. One is the maximum likelihood hypothesis testing method based on decision theory, the other is a statistical pattern recognition method based on feature extraction. Now, the most commonly used is a statistical pattern recognition method, which includes feature extraction and classifier design. With the increasingly complex electromagnetic environment of communications, how to effectively extract the features of various signals at low signal-to-noise ratio (SNR) is a hot topic for scholars in various countries. To solve this problem, this paper proposes a feature extraction algorithm for the communication signal based on the improved Holder cloud feature. And the extreme learning machine (ELM) is used which aims at the problem of the real-time in the modern warfare to classify the extracted features. The algorithm extracts the digital features of the improved cloud model without deterministic information in a low SNR environment, and uses the improved cloud model to obtain more stable Holder cloud features and the performance of the algorithm is improved. This algorithm addresses the problem that a simple feature extraction algorithm based on Holder coefficient feature is difficult to recognize at low SNR, and it also has a better recognition accuracy. The results of simulations show that the approach in this paper still has a good classification result at low SNR, even when the SNR is -15dB, the recognition accuracy still reaches 76%.

Keywords: communication signal, feature extraction, Holder coefficient, improved cloud model

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Authors: Sujeet Kumar

Abstract:

Education has greater importance particularly in terms of increasing human capital and economic competitiveness. It plays a crucial role in terms of cognitive and skill development. Its plays a vital role in process of socialization, fostering social justice, and enhancing social cohesion. Policy related to education has been always a priority for developed countries, which is later adopted by developing countries also. The government of India has also brought change in education polices in line with recognizing change at national and supranational level. However, quality education is still not become an open door for every child in India and several reports are produced year to year about level of school education in India. This paper is concerned with schooling in India. Particularly, it focuses on two government and two private schools in Bihar, but reference has made to schools in Delhi especially around slum communities. The paper presents brief historical context and an overview of current school systems in India. Later, it focuses on analysis of current development in policy in reference with field observation, which is anchored around choice, diversity, market – orientation and gap between different groups of pupils. There is greater degree of difference observed at private and government school levels in terms of quality of teachers, method of teaching and overall environment of learning. The paper concludes that the recent policy development in education particularly Sarva Siksha Abhiyaan (SAA) and Right to Education Act (2009) has required renovating new approach to bridge the gap through broader consultation at grassroots and participatory approach with different stakeholders.

Keywords: education, public policy, participatory approach

Procedia PDF Downloads 382

Authors: Rana Alaa El-Deen Ahmed, M. Elemam Shehab, Shereen Morsy, Nermeen Mekawie

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With the growing popularity and acceptance of e-commerce platforms, users face an ever increasing burden in actually choosing the right product from the large number of online offers. Thus, techniques for personalization and shopping guides are needed by users. For a pleasant and successful shopping experience, users need to know easily which products to buy with high confidence. Since selling a wide variety of products has become easier due to the popularity of online stores, online retailers are able to sell more products than a physical store. The disadvantage is that the customers might not find products they need. In this research the customer will be able to find the products he is searching for, because recommender systems are used in some ecommerce web sites. Recommender system learns from the information about customers and products and provides appropriate personalized recommendations to customers to find the needed product. In this paper eleven classification algorithms are comparatively tested to find the best classifier fit for consumer online shopping attitudes and behavior in the experimented dataset. The WEKA knowledge analysis tool, which is an open source data mining workbench software used in comparing conventional classifiers to get the best classifier was used in this research. In this research by using the data mining tool (WEKA) with the experimented classifiers the results show that decision table and filtered classifier gives the highest accuracy and the lowest accuracy classification via clustering and simple cart.

Keywords: classification, data mining, machine learning, online shopping, WEKA

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Authors: Bathmajaa Muralisankar

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As much as railway station infrastructure designs and ticket-booking norms have been changed to facilitate use by differently abled persons, the railway train compartments themselves have not been made user-friendly for differently abled persons. Owing to safety concerns, dependency on others for their travel, and fear of isolation, differently abled people do not prefer travelling by train. Rather than including a dedicated compartment open only to the differently abled, including the latter with others in the normal compartment (with the proposed modifications discussed here) will make them feel secure and make for an enhanced travel experience for them. This approach also represents the most practical way to include a particular category of people in the mainstream society. Lowering the height of the compartment doors and providing a wider entrance with a ramp will provide easy entry for those using wheelchairs. As well, removing the first two alternate rows and the first two side seats will not only widen the passage and increase seating space but also improve wheelchair turning radius. This will help them travel without having to depend on others. Seating arrangements may be done to accommodate their family members near them instead of isolating the differently abled in a separate compartment. According to present ticket-booking regulations of the Indian Railways, three to four disabled persons may travel without their family or one to two along with their family, and the numbers may be added or reduced. To help visually challenged and hearing-impaired persons, in addition to the provision of special instruments, railings, and textured footpaths and flooring, the seat numbers above the seats may be set in metal or plastic as an outward projection so the visually impaired can touch and feel the numbers. Braille boards may be included at the entrance to the compartment along with seat numbers in the aforementioned projected manner. These seat numbers may be designed as buttons, which when pressed results in an announcement of the seat number in the applicable local language as well as English. Emergency buttons, rather than emergency chains, within the easy reach of disabled passengers will also help them.

Keywords: dependency, differently abled, inclusion, mainstream society

Procedia PDF Downloads 247

Authors: Virender Singh, Mathew Rees, Simon Hampton, Sivaram Annadurai

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Plant identification is a challenging task that aims to identify the family, genus, and species according to plant morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users narrow down the possibilities. However, numerous morphological similarities between and within species render correct classification difficult. In this paper, we tested custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88,000 provided by the Royal Horticultural Society (RHS) and a smaller dataset of 16,000 images from the PlantClef 2015 dataset for classifying plants at genus and species levels, respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420 and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. For classifying plants at the species level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success. In conclusion, these results could help end users, professionals and the general public alike in identifying plants quicker and with improved accuracy.

Keywords: plant identification, CNN, image processing, vision transformer, classification

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Authors: Muhammad Zafarullah Khan, Sumeera Abbasi

Abstract:

The Farmers Field School (FFS) learning approach aims to improve knowledge of the farmers through integrated crop management and provide leadership in their decision making process. The study was conducted to assess the impact of FFS on vegetables production before and after FFS intervention in four villages of district Peshawar in cropping season 2012, by interviewing 80 FFS respondents, twenty from each selected village. It was observed from the study results that all the respondents were satisfied from the impact of FFS and they informed an increased in production in vegetables. It was further observed that after the implementation of FFS the sowing seed rate of tomato and cucumber were decreased from 0.185kg/kanal to 0.100 kg/ kanal and 0.120kg/kanal to 0.010kg/kanal where as the production of tomato and cucumber were increased from 8158.75kgs/kanal to 10302. 5kgs/kanal and 3230kgs/kanal to 5340kgs/kanal, respectively. The cost of agriculture inputs per kanal including seed cost, crop management, Farm Yard Manure, and weedicides in case of tomato were reduced by Rs.28, Rs. 3170, Rs.658and Rs 205 whereas in cucumber reduced by Rs.35, Rs.570, Rs 80 and Rs.430 respectively. Only fertilizers cost was increased by Rs. 2200 in case of tomato and Rs 465 in case of cucumber. Overall the cost was reduced to Rs 545 in tomato and Rs 490 in cucumber production.FFS provided a healthy vegetables and also reduced input cost by adopting integrated crop management. Therefore the promotion of FFS is needed to be planned for farmers to reduce cost of production, so that the more farmers should be benefited.

Keywords: impact, farmer field schools, vegetable production, Peshawar Khyber Pakhtunkhwa

Procedia PDF Downloads 248

Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-nearest neighbors algorithm, radial basis function neural network, red blood cells, support vector machine

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Authors: Mateusz Jeżowski, Jadwiga Fila, Paweł Poszytek

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This abstract summarizes the combined findings of several analyses carried out by the authors on the barriers to accessing international education mobility programs by people with fewer opportunities, especially those with a low level of cultural and social capital. Two flagship educational mobility initiatives of the European Union – the Erasmus+ Program and the European Solidarity Corps are aimed at equipping young people and participants of all ages with the qualifications and skills needed for their meaningful participation in a democratic society intercultural understanding and successful transition in the labour market. The heart of these programs is to come closer to people with fewer opportunities, including people with disabilities, migrants, as well as those facing socio-economic difficulties and displaying a low level of social and cultural capital. Still, people who participate in such initiatives usually demonstrate higher than average cultural and social capital, as understood by Pierre Bourdieu. First of all, the educational attainment of their parents is higher than the average. Secondly, they mostly live in large agglomerations, with good access to education and culture, which affects their foreign language skills as well as social and cultural competencies. Finally, participation in Erasmus+ Program or European Solidarity Corps is not their first educational mobility experience. It is therefore justified to state that their social and cultural capital, already high before taking part in Erasmus+ and European Solidarity Corps, becomes even higher once their international mobility activities have been over, at the expense of those people with fewer opportunities, who, in theory, could participate in those initiatives, nonetheless did not, for the following reasons: lack of sufficient information on such programs, financial obstacles or unappreciation of the value of international mobility. In their work, the authors will discuss what are, in the light of Bourdieu’s perception of social and cultural capital, the main obstacles for young people to participate in international mobility programs of the European Union and will offer comprehensive solutions rooted in their vast experience in management and implementation of Erasmus+ Program and European Solidarity Corps.

Keywords: cultural capital, educational mobility, Erasmus+, European solidarity corps, inclusion, social capital

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Authors: Derjew Ayele Ejigu, Houde Song, Xiaojing Liu

Abstract:

This work presents the particle swarm optimization trained neural network (PSO-NN) supervisory proportional integral derivative (PID) control method to monitor the pressurized water reactor (PWR) core power for safe operation. The proposed control approach is implemented on the transfer function of the PWR core, which is computed from the state-space model. The PWR core state-space model is designed from the neutronics, thermal-hydraulics, and reactivity models using perturbation around the equilibrium value. The proposed control approach computes the control rod speed to maneuver the core power to track the reference in a closed-loop scheme. The particle swarm optimization (PSO) algorithm is used to train the neural network (NN) and to tune the PID simultaneously. The controller performance is examined using integral absolute error, integral time absolute error, integral square error, and integral time square error functions, and the stability of the system is analyzed by using the Bode diagram. The simulation results indicated that the controller shows satisfactory performance to control and track the load power effectively and smoothly as compared to the PSO-PID control technique. This study will give benefit to design a supervisory controller for nuclear engineering research fields for control application.

Keywords: machine learning, neural network, pressurized water reactor, supervisory controller

Procedia PDF Downloads 143