Search results for: computer assisted learning

Authors: Tassadit Issaadi Hamitouche, Nicolas Gillard, Jean Petit, Valerie Lavaste, Celine Mayousse

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Water is a fundamental resource for the industry. It is taken from the environment either from municipal distribution networks or from various natural water sources such as the sea, ocean, rivers, aquifers, etc. Once used, water is discharged into the environment, reprocessed at the plant or treatment plants. These withdrawals and discharges have a direct impact on natural water resources. These impacts can apply to the quantity of water available, the quality of the water used, or to impacts that are more complex to measure and less direct, such as the health of the population downstream from the watercourse, for example. Based on the analysis of data (meteorological, river characteristics, physicochemical substances), we wish to predict water stress episodes and anticipate prefectoral decrees, which can impact the performance of plants and propose improvement solutions, help industrialists in their choice of location for a new plant, visualize possible interactions between companies to optimize exchanges and encourage the pooling of water treatment solutions, and set up circular economies around the issue of water. The development of a system for the collection, processing, and use of data related to water resources requires the functional constraints specific to the latter to be made explicit. Thus the system will have to be able to store a large amount of data from sensors (which is the main type of data in plants and their environment). In addition, manufacturers need to have 'near-real-time' processing of information in order to be able to make the best decisions (to be rapidly notified of an event that would have a significant impact on water resources). Finally, the visualization of data must be adapted to its temporal and geographical dimensions. In this study, we set up an infrastructure centered on the TICK application stack (for Telegraf, InfluxDB, Chronograf, and Kapacitor), which is a set of loosely coupled but tightly integrated open source projects designed to manage huge amounts of time-stamped information. The software architecture is coupled with the cross-industry standard process for data mining (CRISP-DM) data mining methodology. The robust architecture and the methodology used have demonstrated their effectiveness on the study case of learning the level of a river with a 7-day horizon. The management of water and the activities within the plants -which depend on this resource- should be considerably improved thanks, on the one hand, to the learning that allows the anticipation of periods of water stress, and on the other hand, to the information system that is able to warn decision-makers with alerts created from the formalization of prefectoral decrees.

Keywords: data mining, industry, machine Learning, shortage, water resources

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Authors: Meng Su

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High-dimensional data analysis often presents challenges in capturing the complex, nonlinear relationships and manifold structures inherent to the data. This article presents a novel approach that leverages the strengths of two powerful techniques, Diffusion Maps and Diffusion Probabilistic Models (DPMs), to address these challenges. By integrating the dimensionality reduction capability of Diffusion Maps with the data modeling ability of DPMs, the proposed method aims to provide a comprehensive solution for analyzing and generating high-dimensional data. The Diffusion Map technique preserves the nonlinear relationships and manifold structure of the data by mapping it to a lower-dimensional space using the eigenvectors of the graph Laplacian matrix. Meanwhile, DPMs capture the dependencies within the data, enabling effective modeling and generation of new data points in the low-dimensional space. The generated data points can then be mapped back to the original high-dimensional space, ensuring consistency with the underlying manifold structure. Through a detailed example implementation, the article demonstrates the potential of the proposed hybrid approach to achieve more accurate and effective modeling and generation of complex, high-dimensional data. Furthermore, it discusses possible applications in various domains, such as image synthesis, time-series forecasting, and anomaly detection, and outlines future research directions for enhancing the scalability, performance, and integration with other machine learning techniques. By combining the strengths of Diffusion Maps and DPMs, this work paves the way for more advanced and robust data analysis methods.

Keywords: diffusion maps, diffusion probabilistic models (DPMs), manifold learning, high-dimensional data analysis

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Authors: Tianqi Wu, Min Wang

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Research investigating language acquisition from a constructionist perspective has demonstrated that language is learned as constructions at various linguistic levels, which is related to factors of frequency, semantic prototypicality, and form-meaning contingency. However, previous research on construction learning tended to focus on clause-level constructions such as verb argument constructions but few attempts were made to study morpheme-level constructions such as the progressive construction, which is regarded as a source of acquisition problems for English learners from diverse L1 backgrounds, especially for those whose L1 do not have an equivalent construction such as German and Chinese. To trace the developmental trajectory of Chinese EFL learners’ use of the progressive with respect to verb frequency, verb-progressive contingency, and verbal prototypicality and generality, a learner corpus consisting of three sub-corpora representing three different English proficiency levels was extracted from the Chinese Learners of English Corpora (CLEC). As the reference point, a native speakers’ corpus extracted from the Louvain Corpus of Native English Essays was also established. All the texts were annotated with C7 tagset by part-of-speech tagging software. After annotation all valid progressive hits were retrieved with AntConc 3.4.3 followed by a manual check. Frequency-related data showed that from the lowest to the highest proficiency level, (1) the type token ratio increased steadily from 23.5% to 35.6%, getting closer to 36.4% in the native speakers’ corpus, indicating a wider use of verbs in the progressive; (2) the normalized entropy value rose from 0.776 to 0.876, working towards the target score of 0.886 in native speakers’ corpus, revealing that upper-intermediate learners exhibited a more even distribution and more productive use of verbs in the progressive; (3) activity verbs (i.e., verbs with prototypical progressive meanings like running and singing) dropped from 59% to 34% but non-prototypical verbs such as state verbs (e.g., being and living) and achievement verbs (e.g., dying and finishing) were increasingly used in the progressive. Apart from raw frequency analyses, collostructional analyses were conducted to quantify verb-progressive contingency and to determine what verbs were distinctively associated with the progressive construction. Results were in line with raw frequency findings, which showed that contingency between the progressive and non-prototypical verbs represented by light verbs (e.g., going, doing, making, and coming) increased as English proficiency proceeded. These findings altogether suggested that beginning Chinese EFL learners were less productive in using the progressive construction: they were constrained by a small set of verbs which had concrete and typical progressive meanings (e.g., the activity verbs). But with English proficiency increasing, their use of the progressive began to spread to marginal members such as the light verbs.

Keywords: Construction learning, Corpus-based, Progressives, Prototype

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Authors: Bernard Igoche Igoche, Olumuyiwa Matthew, Peter Bednar, Alexander Gegov

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This study presents an ontology-driven approach for knowledge discovery and validation from admission databases in Nigerian polytechnic institutions. The research aims to address the challenges of extracting meaningful insights from vast amounts of admission data and utilizing them for decision-making and process improvement. The proposed methodology combines the knowledge discovery in databases (KDD) process with a structural causal model (SCM) ontological framework. The admission database of Benue State Polytechnic Ugbokolo (Benpoly) is used as a case study. The KDD process is employed to mine and distill knowledge from the database, while the SCM ontology is designed to identify and validate the important features of the admission process. The SCM validation is performed using the conditional independence test (CIT) criteria, and an algorithm is developed to implement the validation process. The identified features are then used for machine learning (ML) modeling and prediction of admission status. The results demonstrate the adequacy of the SCM ontological framework in representing the admission process and the high predictive accuracies achieved by the ML models, with k-nearest neighbors (KNN) and support vector machine (SVM) achieving 92% accuracy. The study concludes that the proposed ontology-driven approach contributes to the advancement of educational data mining and provides a foundation for future research in this domain.

Keywords: admission databases, educational data mining, machine learning, ontology-driven knowledge discovery, polytechnic education, structural causal model

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Authors: Nor Zaidi Haron, Fauziyah Salehuddin, Norsuhaidah Arshad, Sani Irwan Salim

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Resistive random access memories (RRAMs) are one of the main candidates for future computer memories. However, due to their tiny size and immature device technology, the quality of the outgoing RRAM chips is seen as a serious issue. Defective RRAM cells might behave differently than existing semiconductor memories (Dynamic RAM, Static RAM, and Flash), meaning that they are difficult to be detected using existing test schemes. This paper presents an enhanced test scheme, referred to as Programmable Short Write Time (PSWT) that is able to improve the detection of faulty RRAM cells. It is developed by applying multiple weak write operations, each with different time durations. The test circuit embedded in the RRAM chip is made programmable in order to supply different weak write times during testing. The RRAM electrical model is described using Verilog-AMS language and is simulated using HSPICE simulation tools. Simulation results show that the proposed test scheme offers better open-resistive fault detection compared to existing test schemes.

Keywords: memory fault, memory test, design-for-testability, resistive random access memory

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Authors: Noriyuki Suyama

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The progress and spread of digitalization have led to the provision of a variety of new services. The recent progress in digitization can be attributed to rapid developments in science and technology. First, the research and diffusion of artificial intelligence (AI) has made dramatic progress. Around 2000, the third wave of AI research, which had been underway for about 50 years, arrived. Specifically, machine learning and deep learning were made possible in AI, and the ability of AI to acquire knowledge, define the knowledge, and update its own knowledge in a quantitative manner made the use of big data practical even for commercial PCs. On the other hand, with the spread of social media, information exchange has become more common in our daily lives, and the lending and borrowing of goods and services, in other words, the sharing economy, has become widespread. The scope of this trend is not limited to any industry, and its momentum is growing as the SDGs take root. In addition, the Social Network Service (SNS), a part of social media, has brought about the evolution of the retail business. In the past few years, social network services (SNS) involving users or companies have especially flourished. The People's Republic of China (hereinafter referred to as "China") is a country that is stimulating enormous consumption through its own unique SNS, which is different from the SNS used in developed countries around the world. This paper focuses on the effectiveness and challenges of influencer marketing by focusing on the influence of influencers on users' behavior and satisfaction with Chinese SNSs. Specifically, Conducted was the quantitative survey of Tik Tok users living in China, with the aim of gaining new insights from the analysis and discussions. As a result, we found several important findings and knowledge.

Keywords: customer satisfaction, social networking services, influencer marketing, Chinese consumers’ behavior

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Authors: Odoh Ben Uruchi

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Information Technology is an outcome of the nexus between the computer technology and the communication technology which has grown as silver fiber in Nigeria. Information Technology represents the fourth generation of human communication after sight, oral and written communications. The internet, as with all path-breaking technological developments gives us all the ample privileges to act as a global community; advertise and operate across all frontiers; over boarders and beyond the control of any government. The security concerns, computer abuse and the side effects of this technology have moved to the forefront of the consciousness of law enforcement agencies. Unfortunately, Nigeria is one of the very few countries in the world to have not legislated Cyber Laws, although several unsuccessful attempts have been made in recent times at providing the legal framework for regulating the activities in Nigerian cyberspace. Traditional legal systems have led to great difficulty in keeping pace with the rapid growth of the internet and its impact throughout Nigeria. The only existing legal frameworks are constantly being challenged by technological advancement. This has created a need to constantly update and adapt the way in which we organize ourselves as Legal Practitioners in order to maintain overall control of its domestic and national interests. This paper seeks to appraise the challenges facing the legal profession in Nigeria because of want of Cyber Laws. In doing this, the paper shall highlight the loopholes in the existing laws and recommends the way forward.

Keywords: information technology, challenges, legal profession, Nigeria

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Authors: Carol S. Binnekade, Deon Scott, Christina C. Shuttleworth, Annelien A. Van Rooyen

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Higher education institutions are constantly challenged to deliver skilled graduates into the workplace. Employers expect graduates to have the required technical knowledge as well as various pervasive skills. This also applies to accountants who need to know the technical requirements of financial reporting and be able to communicate with individuals, teams and clients at a high level. Accountants need to develop effective business conversational skills and use these skills to communicate up, down and across organizations, taking into consideration cultural and gender diversity. In addition, they need to master business writing and presentation skills. However, providing students with these skills in a distance-learning environment where interaction between students and instructors is limited, is a challenge for academics. The study on which this paper reports, forms part of a larger body of research, which explored the perceptions of accounting practitioners of the communication skills (or lack thereof) of recently qualified accounting students. Feedback (qualitative and quantitative) was obtained from various accounting practitioners in South Africa. Taking into consideration that distance learners communicate mainly with their instructors via email communication and their assignments are submitted using various word processor software, the researchers were of the opinion that the accounting graduates would be capable of communicating effectively once they entered the workplace. However, the research findings, inter alia, suggested that the accounting graduates lacked communication skills and that training was needed to differentiate between business and social communication once they entered the workplace. Recommendations on how these communication challenges may be addressed by higher education institutions are provided.

Keywords: accounting practitioners, communication skills, distance education, pervasive skills

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Authors: Alvin Uchenna Ugwu

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The United Nation’s 2030 agenda and Global Action Programme (GAP) for implementation of the Sustainable Development Goals (SDGs), has mandated all sectors in the societies, including education, to develop strategies towards actualizing sustainability in all facets of the society, by the year 2030. Education is no doubt a key tool for social change. However, educational institutions in most African nations need a paradigmatic shift to strike a balance between policies (curricular) and practices, with regards to Education for Sustainable Development (ESD). The paradigm shift in this regard is described as whole-institution/school approach. The whole institution approaches advocate action-focused ESD. In other words, ESD policy and curriculum makers, formal and non-formal education institutions, need to ‘practice what they preach’. This paper is developed from an ongoing study carried out by the author and guided by two research questions: -What are the views of intermediate phase science and technology preservice teachers on the ESD content included in the science and technology modules? -What challenges or enable intermediate phase science and technology pre-service teachers to learn about ESD in science and technology modules? The study drew from the views and experiences of preservice science teachers, learning about ESD in a university’s college of education in South Africa. Using qualitative case study research design, the research data were generated via questionnaires and focus group discussions. Analysis of generated data indicates that universities and institutions of higher learning need to demonstrate practical involvement while implementing ESD in societies, rather than just standing as knowledge media. Findings of the study further suggest that natural sciences and technology courses in teacher education programmes and other institutions of higher learning, should be perceived as key transformative tools in shaping the consciousness of students towards integrating and fostering ESD in developing countries such as South Africa. Thus, this paper seeks to promote ‘Whole Institution Involvement’ in teacher education colleges in South Africa, as a measure of improving ESD in higher education settings. The paper suggests that in order to achieve ESD in higher education settings and beyond, policies and practices should be reexamined beyond rhetoric and buzzwords. The paper further argues that implementation of ESD is largely influenced by context, hence two different contexts should be examined empirically.

Keywords: education for sustainable development, higher education institutions, pre-service science teachers, qualitative case study research, whole institution involvement

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Authors: Jeremy Jenrette, Z. Y. C. Liu, Pranav Chimote, Edward Fox, Trevor Hastie, Francesco Ferretti

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Suitable shark conservation depends on well-informed population assessments. Direct methods such as scientific surveys and fisheries monitoring are adequate for defining population statuses, but species-specific indices of abundance and distribution coming from these sources are rare for most shark species. We can rapidly fill these information gaps by boosting media-based remote monitoring efforts with machine learning and automation. We created a database of shark images by sourcing 24,546 images covering 219 species of sharks from the web application spark pulse and the social network Instagram. We used object detection to extract shark features and inflate this database to 53,345 images. We packaged object-detection and image classification models into a Shark Detector bundle. We developed the Shark Detector to recognize and classify sharks from videos and images using transfer learning and convolutional neural networks (CNNs). We applied these models to common data-generation approaches of sharks: boosting training datasets, processing baited remote camera footage and online videos, and data-mining Instagram. We examined the accuracy of each model and tested genus and species prediction correctness as a result of training data quantity. The Shark Detector located sharks in baited remote footage and YouTube videos with an average accuracy of 89\%, and classified located subjects to the species level with 69\% accuracy (n =\ eight species). The Shark Detector sorted heterogeneous datasets of images sourced from Instagram with 91\% accuracy and classified species with 70\% accuracy (n =\ 17 species). Data-mining Instagram can inflate training datasets and increase the Shark Detector’s accuracy as well as facilitate archiving of historical and novel shark observations. Base accuracy of genus prediction was 68\% across 25 genera. The average base accuracy of species prediction within each genus class was 85\%. The Shark Detector can classify 45 species. All data-generation methods were processed without manual interaction. As media-based remote monitoring strives to dominate methods for observing sharks in nature, we developed an open-source Shark Detector to facilitate common identification applications. Prediction accuracy of the software pipeline increases as more images are added to the training dataset. We provide public access to the software on our GitHub page.

Keywords: classification, data mining, Instagram, remote monitoring, sharks

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Authors: Raptis Sotirios

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Health and social care (HSc) services planning and scheduling are facing unprecedented challenges due to the pandemic pressure and also suffer from unplanned spending that is negatively impacted by the global financial crisis. Data-driven can help to improve policies, plan and design services provision schedules using algorithms assist healthcare managers’ to face unexpected demands using fewer resources. The paper discusses services packing using statistical significance tests and machine learning (ML) to evaluate demands similarity and coupling. This is achieved by predicting the range of the demand (class) using ML methods such as CART, random forests (RF), and logistic regression (LGR). The significance tests Chi-Squared test and Student test are used on data over a 39 years span for which HSc services data exist for services delivered in Scotland. The demands are probabilistically associated through statistical hypotheses that assume that the target service’s demands are statistically dependent on other demands as a NULL hypothesis. This linkage can be confirmed or not by the data. Complementarily, ML methods are used to linearly predict the above target demands from the statistically found associations and extend the linear dependence of the target’s demand to independent demands forming, thus groups of services. Statistical tests confirm ML couplings making the prediction also statistically meaningful and prove that a target service can be matched reliably to other services, and ML shows these indicated relationships can also be linear ones. Zero paddings were used for missing years records and illustrated better such relationships both for limited years and in the entire span offering long term data visualizations while limited years groups explained how well patients numbers can be related in short periods or can change over time as opposed to behaviors across more years. The prediction performance of the associations is measured using Receiver Operating Characteristic(ROC) AUC and ACC metrics as well as the statistical tests, Chi-Squared and Student. Co-plots and comparison tables for RF, CART, and LGR as well as p-values and Information Exchange(IE), are provided showing the specific behavior of the ML and of the statistical tests and the behavior using different learning ratios. The impact of k-NN and cross-correlation and C-Means first groupings is also studied over limited years and the entire span. It was found that CART was generally behind RF and LGR, but in some interesting cases, LGR reached an AUC=0 falling below CART, while the ACC was as high as 0.912, showing that ML methods can be confused padding or by data irregularities or outliers. On average, 3 linear predictors were sufficient, LGR was found competing RF well, and CART followed with the same performance at higher learning ratios. Services were packed only if when significance level(p-value) of their association coefficient was more than 0.05. Social factors relationships were observed between home care services and treatment of old people, birth weights, alcoholism, drug abuse, and emergency admissions. The work found that different HSc services can be well packed as plans of limited years, across various services sectors, learning configurations, as confirmed using statistical hypotheses.

Keywords: class, cohorts, data frames, grouping, prediction, prob-ability, services

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Authors: A. Soni, D. R. Mishra, D. K. Koul

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α-Al2O3:C has been reported to have deeper traps at 600°C and 900°C respectively. These traps have been reported to accessed at relatively earlier temperatures (122 and 322 °C respectively) using thermally assisted OSL (TA-OSL). In this work, the dose response α-Al2O3:C was studied in the dose range of 10Gy to 10kGy for its application in food irradiation in low ( upto 1kGy) and medium(1 to 10kGy) dose range. The TOL (Thermo-optically stimulated luminescence) measurements were carried out on RisØ TL/OSL, TL-DA-15 system having a blue light-emitting diodes (λ=470 ±30nm) stimulation source with power level set at the 90% of the maximum stimulation intensity for the blue LEDs (40 mW/cm2). The observations were carried on commercial α-Al2O3:C phosphor. The TOL experiments were carried out with number of active channel (300) and inactive channel (1). Using these settings, the sample is subjected to linear thermal heating and constant optical stimulation. The detection filter used in all observations was a Hoya U-340 (Ip ~ 340 nm, FWHM ~ 80 nm). Irradiation of the samples was carried out using a 90Sr/90Y β-source housed in the system. A heating rate of 2 °C/s was preferred in TL measurements so as to reduce the temperature lag between the heater plate and the samples. To study the dose response of deep traps of α-Al2O3:C, samples were irradiated with various dose ranging from 10 Gy to 10 kGy. For each set of dose, three samples were irradiated. In order to record the TA-OSL, initially TL was recorded up to a temperature of 400°C, to deplete the signal due to 185°C main dosimetry TL peak in α-Al2O3:C, which is also associated with the basic OSL traps. After taking TL readout, the sample was subsequently subjected to TOL measurement. As a result, two well-defined TA-OSL peaks at 121°C and at 232°C occur in time as well as temperature domain which are different from the main dosimetric TL peak which occurs at ~ 185°C. The linearity of the integrated TOL signal has been measured as a function of absorbed dose and found to be linear upto 10kGy. Thus, it can be used for low and intermediate dose range of for its application in food irradiation. The deep energy level defects of α-Al2O3:C phosphor can be accessed using TOL section of RisØ reader system.

Keywords: α-Al2O3:C, deep traps, food irradiation, TA-OSL

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Authors: Larissa Bolliger, Junoš Lukan, Mitja Lustrek, Dirk De Bacquer, Els Clays

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Introduction and Aim: Chronic workplace stress and its health-related consequences like mental and cardiovascular diseases have been widely investigated. This project focuses on the sources and context of psychosocial daily workplace stress in a real-world setting. The main objective is to analyze and model real-time relationships between (1) psychosocial stress experiences within the natural work environment, (2) micro-level work activities and events, and (3) physiological signals and behaviors in office workers. Methods: An Ecological Momentary Assessment (EMA) protocol has been developed, partly building on machine learning techniques. Empatica® wristbands will be used for real-life detection of stress from physiological signals; micro-level activities and events at work will be based on smartphone registrations, further processed according to an automated computer algorithm. A field study including 100 office-based workers with high-level problem-solving tasks like managers and researchers will be implemented in Slovenia and Belgium (50 in each country). Data mining and state-of-the-art statistical methods – mainly multilevel statistical modelling for repeated data – will be used. Expected Results and Impact: The project findings will provide novel contributions to the field of occupational health research. While traditional assessments provide information about global perceived state of chronic stress exposure, the EMA approach is expected to bring new insights about daily fluctuating work stress experiences, especially micro-level events and activities at work that induce acute physiological stress responses. The project is therefore likely to generate further evidence on relevant stressors in a real-time working environment and hence make it possible to advise on workplace procedures and policies for reducing stress.

Keywords: ecological momentary assessment, real-time, stress, work

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Authors: M. Taheri Tehrani, H. Ajorloo

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In this paper, an intelligent multi-agent framework is developed for each router in which agents have two vital functionalities, traffic shaping and buffer allocation and are positioned in the ports of the routers. With traffic shaping functionality agents shape the traffic forward by dynamic and real time allocation of the rate of generation of tokens in a Token Bucket algorithm and with buffer allocation functionality agents share their buffer capacity between each other based on their need and the conditions of the network. This dynamic and intelligent framework gives this opportunity to some ports to work better under burst and more busy conditions. These agents work intelligently based on Reinforcement Learning (RL) algorithm and will consider effective parameters in their decision process. As RL have limitation considering much parameter in its decision process due to the volume of calculations, we utilize our novel method which invokes Principle Component Analysis (PCA) on the RL and gives a high dimensional ability to this algorithm to consider as much as needed parameters in its decision process. This implementation when is compared to our previous work where traffic shaping was done without any sharing and dynamic allocation of buffer size for each port, the lower packet drop in the whole network specifically in the source routers can be seen. These methods are implemented in our previous proposed intelligent simulation environment to be able to compare better the performance metrics. The results obtained from this simulation environment show an efficient and dynamic utilization of resources in terms of bandwidth and buffer capacities pre allocated to each port.

Keywords: principal component analysis, reinforcement learning, buffer allocation, multi- agent systems

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Authors: Despoina Mouratidis, Katia Lida Kermanidis

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Machine translation (MT) is a very important task in Natural Language Processing (NLP). MT evaluation is crucial in MT development, as it constitutes the means to assess the success of an MT system, and also helps improve its performance. Several methods have been proposed for the evaluation of (MT) systems. Some of the most popular ones in automatic MT evaluation are score-based, such as the BLEU score, and others are based on lexical similarity or syntactic similarity between the MT outputs and the reference involving higher-level information like part of speech tagging (POS). This paper presents a language-independent machine learning framework for classifying pairwise translations. This framework uses vector representations of two machine-produced translations, one from a statistical machine translation model (SMT) and one from a neural machine translation model (NMT). The vector representations consist of automatically extracted word embeddings and string-like language-independent features. These vector representations used as an input to a multi-layer neural network (NN) that models the similarity between each MT output and the reference, as well as between the two MT outputs. To evaluate the proposed approach, a professional translation and a "ground-truth" annotation are used. The parallel corpora used are English-Greek (EN-GR) and English-Italian (EN-IT), in the educational domain and of informal genres (video lecture subtitles, course forum text, etc.) that are difficult to be reliably translated. They have tested three basic deep learning (DL) architectures to this schema: (i) fully-connected dense, (ii) Convolutional Neural Network (CNN), and (iii) Long Short-Term Memory (LSTM). Experiments show that all tested architectures achieved better results when compared against those of some of the well-known basic approaches, such as Random Forest (RF) and Support Vector Machine (SVM). Better accuracy results are obtained when LSTM layers are used in our schema. In terms of a balance between the results, better accuracy results are obtained when dense layers are used. The reason for this is that the model correctly classifies more sentences of the minority class (SMT). For a more integrated analysis of the accuracy results, a qualitative linguistic analysis is carried out. In this context, problems have been identified about some figures of speech, as the metaphors, or about certain linguistic phenomena, such as per etymology: paronyms. It is quite interesting to find out why all the classifiers led to worse accuracy results in Italian as compared to Greek, taking into account that the linguistic features employed are language independent.

Keywords: machine learning, machine translation evaluation, neural network architecture, pairwise classification

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Authors: Mohammad Sheikhalishahi, Vahid Ebrahimipour, Amir Hossein Radman-Kian

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This paper proposes an Ontology framework for knowledge modeling and representation of the equipment integrity process in a typical oil and gas production plant. Our aim is to construct a knowledge modeling that facilitates translation, interpretation, and conversion of human-readable integrity interpretation into computer-readable representation. The framework provides a function structure related to fault propagation using ISO 14224 and ISO 15926 OWL-Lite/ Resource Description Framework (RDF) to obtain a generic system-level model of asset integrity that can be utilized in the integrity engineering process during the equipment life cycle. It employs standard terminology developed by ISO 15926 and ISO 14224 to map textual descriptions of equipment failure and then convert it to a causality-driven logic by semantic interpretation and computer-based representation using Lite/RDF. The framework applied for an offshore gas riser. The result shows that the approach can cross-link the failure-related integrity words and domain-specific logic to obtain a representation structure of equipment integrity with causality inference based on semantic extraction of inspection report context.

Keywords: asset integrity modeling, interoperability, OWL, RDF/XML

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Authors: Ashley Hobson, Ashley Efaw

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Professionalism is an essential skill for clinicians, particularly for Occupational Therapy Providers (OTPs). The World Federation of Occupational Therapy (WFOT) Guiding Principles for Ethical Occupational Therapy and American Occupational Therapy Association (AOTA) Code of Ethics establishes expectations for professionalism among OTPs, emphasizing its importance in the field. However, the teaching and assessment of professionalism vary across OTP programs. The flexibility provided by the country standards allows programs to determine their own approaches to meeting these standards, resulting in inconsistency. Educators in both academic and fieldwork settings face challenges in objectively assessing and providing feedback on student professionalism. Although they observe instances of unprofessional behavior, there is no standardized assessment measure to evaluate professionalism in OTP students. While most students are committed to learning and applying professionalism skills, they enter OTP programs with varying levels of proficiency in this area. Consequently, they lack a uniform understanding of professionalism and lack an objective means to self-assess their current skills and identify areas for growth. It is crucial to explicitly teach professionalism, have students to self-assess their professionalism skills, and have OTP educators assess student professionalism. This approach is necessary for fostering students' professionalism journeys. Traditionally, there has been no objective way for students to self-assess their professionalism or for educators to provide objective assessments and feedback. To establish a uniform approach to professionalism, the authors incorporated professionalism content into our curriculum. Utilizing an operational definition of professionalism, the authors integrated professionalism into didactic, fieldwork, and capstone courses. The complexity of the content and the professionalism skills expected of students increase each year to ensure students graduate with the skills to practice in accordance with the WFOT Guiding Principles for Ethical Occupational Therapy Practice and AOTA Code of Ethics. Two professionalism assessments were developed based on the expectations outlined in the both documents. The Professionalism Self-Assessment allows students to evaluate their professionalism, reflect on their performance, and set goals. The Professionalism Assessment for Educators is a modified version of the same tool designed for educators. The purpose of this workshop is to provide educators with a framework and tools for assessing student professionalism. The authors discuss how to integrate professionalism content into OTP curriculum and utilize professionalism assessments to provide constructive feedback and equitable learning opportunities for OTP students in academic, fieldwork, and capstone settings. By adopting these strategies, educators can enhance the development of professionalism among OTP students, ensuring they are well-prepared to meet the demands of the profession.

Keywords: professionalism, assessments, student learning, student preparedness, ethical practice

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Authors: Fadila Hadjeris

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The study aims at investigating the functions of English-Arabic code switching in English as a foreign language classroom oral discourse and the extent to which they can contribute to the flow of classroom interaction. It also seeks to understand the views, beliefs, and perceptions of teachers and learners towards this practice. We hypothesized that code switching is a communicative strategy which facilitates classroom interaction. Due to this fact, both teachers and learners support its use. The study draws on a key body of literature in bilingualism, second language acquisition, and classroom discourse in an attempt to provide a framework for considering the research questions. It employs a combination of qualitative and quantitative research methods which include classroom observations and questionnaires. The analysis of the recordings shows that teachers’ code switching to Arabic is not only used for academic and classroom management reasons. Rather, the data display instances in which code switching is used for social reasons. The analysis of the questionnaires indicates that teachers and pupils have different attitudes towards this phenomenon. Teachers reported their deliberate switching during EFL teaching, yet the majority was against this practice. According to them, the use of the mother has detrimental effects on the acquisition and the practice of the target language. In contrast, pupils showed their preference to their teachers’ code switching because it enhances and facilitates their understanding. These findings support the fact that the shift to pupils’ mother tongue is a strategy which aids and facilitates the teaching and the learning of the target language. This, in turn, necessitates recommendations which are suggested to teachers and course designers.

Keywords: bilingualism, codeswitching, classroom interaction, classroom discourse, EFL learning/ teaching, SLA

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Authors: Nursel Bektas

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The purpose of this study is to investigate the roles of teaching assistants (TAs) working in reception classes through practitioners’ perspectives. The study has two major purposes; firstly to explore the general roles of TAs, and secondly to identify their roles in supporting children for mathematics. A small-scale case study approach was adopted for this study. The research was carried out in two reception classes within a primary school in London. The qualitative data were gathered through observations and semi-structured interviews with four reception class practitioners, comprising two teachers and two TAs. The results show that TAs consider their role to be more like a teacher, whereas classroom teachers do not corroborate this and they generally believe that the role of TAs depends on their personal characteristics and skills. In regard to the general role of TAs, the study suggests that reception class TAs are deployed both at the classroom level to provide academic support for children’s learning and development, and at the school level they are deployed as support staff such as Midday Meal Supervisor or assistants. In terms of the pedagogical roles of TAs, it was found that TAs have a strong teaching role in literacy development, with notable autonomy if conducting their own phonics sessions without teacher direction, but a negligible influence in numeracy/ math’s. In addition, the results show that the TA role is perceived to be quite limited in planning and assessment processes. Linked to their limited roles in such processes, all participants agree that all the responsibility regarding the children’s learning and development, planning and assessment lies with the teacher. Therefore, data suggest that TAs’ roles in these areas depend on TAs’ their own initiatives.

Keywords: early years education, reception classes, roles, teaching assistants

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Authors: Saikiran Subbagari, Avinash Malladhi

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Anti Money Laundering (AML) regulations are designed to prevent money laundering and terrorist financing activities worldwide. Financial institutions around the world are legally obligated to identify, assess and mitigate the risks associated with money laundering and report any suspicious transactions to governing authorities. With increasing volumes of data to analyze, financial institutions seek to automate their AML processes. In the rise of financial crimes, optical character recognition (OCR), in combination with machine learning (ML) algorithms, serves as a crucial tool for automating AML processes by extracting the data from documents and identifying suspicious transactions. In this paper, we examine the utilization of OCR for AML and delve into various OCR techniques employed in AML processes. These techniques encompass template-based, feature-based, neural network-based, natural language processing (NLP), hidden markov models (HMMs), conditional random fields (CRFs), binarizations, pattern matching and stroke width transform (SWT). We evaluate each technique, discussing their strengths and constraints. Also, we emphasize on how OCR can improve the accuracy of customer identity verification by comparing the extracted text with the office of foreign assets control (OFAC) watchlist. We will also discuss how OCR helps to overcome language barriers in AML compliance. We also address the implementation challenges that OCR-based AML systems may face and offer recommendations for financial institutions based on the data from previous research studies, which illustrate the effectiveness of OCR-based AML.

Keywords: anti-money laundering, compliance, financial crimes, fraud detection, machine learning, optical character recognition

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Authors: Xuan Zheng

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China is home to one of the world’s largest Deaf and Hard of Hearing (DHH) populations. In the 1980s, the concept of inclusive education was introduced, giving rise to a unique “learning in regular class (随班就读)” model tailored to local contexts. China’s inclusive education for DHH students is diversifying with innovative models like special education classes at regular schools, regular classes at regular schools, resource classrooms, satellite classes, and bilingual-bimodal projects. The scope extends to preschool and higher education programs. However, the inclusive development of DHH students faces challenges. The prevailing pathological viewpoint on disabilities persists, emphasizing the necessity for favorable auditory and speech rehabilitation outcomes before DHH students can integrate into regular classes. In addition, inadequate support systems in inclusive schools result in poor academic performance and increased psychological disorders among the group, prompting a notable return to special education schools. Looking ahead, China’s inclusive education for DHH students needs a substantial shift from “learning in regular class” to “sharing equal regular education.” Particular attention should be devoted to the effective integration of DHH students who employ sign language into mainstream educational settings. It is crucial to strengthen regulatory frameworks and institutional safeguards, advance the professional development of educators specializing in inclusive education for DHH students, and consistently enhance resources tailored to this demographic. Furthermore, the establishment of a robust, multidimensional, and collaborative support network, engaging both families and educational institutions, is also a pivotal facet.

Keywords: deaf, hard of hearing, inclusive education, China

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Authors: Hicham Amine, Abdelouahab Mesnaoui

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Electronic data interchange EDI is the computer-to-computer exchange of structured business information. This information typically takes the form of standardized electronic business documents, such as invoices, purchase orders, bills of lading, and so on. The purpose of this study is to identify the impact EDI might have on supply chain and typically on customer satisfaction keeping in mind the constraints the organization might face. This study included 139 subject matter experts (SMEs) who participated by responding to a survey that was distributed. 85% responded that they are extremely for the implementation while 10% were neutral and 5% were against the implementation. From the quality assurance department, we have got 75% from the clients agreed to move on with the change whereas 10% stayed neutral and finally 15% were against the change. From the legal department where 80% of the answers were for the implementation and 10% of the participants stayed neutral whereas the last 10% were against it. The survey consisted of 40% male and 60% female (sex-ratio (F/M=1,5), who had chosen to participate. Our survey also contained 3 categories in terms of technical background where 80% are from technical background and 15% were from nontechnical background and 5% had some average technical background. This study examines the impact of EDI on customer satisfaction which is the primary hypothesis and justifies the importance of the implementation which enhances the customer satisfaction.

Keywords: electronic data interchange, supply chain, subject matter experts, customer satisfaction

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Authors: K. Doi, T. Nishimura, H. Fujimoto, T. Tanaka

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It has been known that visually impaired persons have some problems in getting visual information. Therefore, information accessibility for the visually impaired persons is very important in a current information society. Some application software with read-aloud function for using personal computer and smartphone are getting more and more popular among visually impaired persons in the world. On the other hand, it is also very important for being able to learn how to read and write characters such as Braille and Visual character. Braille typewriter has been widely used in learning Braille. And also raised-line drawing kits as writing material has been used for decades for especially acquired visually impaired persons. However, there are some drawbacks such as the drawn line cannot be erased. Moreover, visibility of drawing lines is not so good for visually impaired with low vision. We had significant number of requests for developing new writing material for especially acquired visually impaired persons instead of raised-line drawing kits. For conducting development research of novel writing material, we could receive a research grant from ministry of health, labor and welfare in Japanese government. In this research, we developed writing material typed pens and pencils with Beeswax-discharge instead of conventional raised-line drawing kits. This writing material was equipped with cartridge heater for melting beeswax and its heat controller. When this pen users held down the pen tip on the regular paper such as fine paper and so on, the melted beeswax could be discharged from pen tip with valve structure. The beeswax was discharged at 100 gf of holding down force based on results of our previous trial study. The shape of pen tip was semispherical for becoming low friction between pen tip and surface of paper. We conducted one basic experiment to evaluate influence of the curvature of pen tip on ease to write. Concretely, the conditions of curvature was 0.15, 0.35, 0.50, 1.00 mm. The following four interval scales were used as indexes of subjective assessment during writing such as feeling of smooth motion of pen, feeling of comfortable writing, sense of security and feeling of writing fatigue. Ten subjects were asked to participate in this experiment. The results reveal that subjects could draw easily when the radius of the pen tip was 1.00 mm, and lines drawn with beeswax-discharge writing material were easy to perceive.

Keywords: beeswax-discharge writing material, raised-line drawing kits, visually impaired persons, pen tip

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Authors: Melike Sultan Karasu Asnaz, Ayse Ozdogan Dolcek

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Crop drying, which aims to reduce the moisture content to a certain level, is a method used to extend the shelf life and prevent it from spoiling. One of the oldest food preservation techniques is open sunor shade drying. Even though this technique is the most affordable of all drying methods, there are some drawbacks such as contamination by insects, environmental pollution, windborne dust, and direct expose to weather conditions such as wind, rain, hail. However, solar dryers that provide a hygienic and controllable environment to preserve food and extend its shelf life have been developed and used to dry agricultural products. Thus, foods can be dried quickly without being affected by weather variables, and quality products can be obtained. This research is mainly devoted to investigating the modelling of drying kinetics of cantaloupe in a forced convection solar dryer. Mathematical models for the drying process should be defined to simulate the drying behavior of the foodstuff, which will greatly contribute to the development of solar dryer designs. Thus, drying experiments were conducted and replicated five times, and various data such as temperature, relative humidity, solar irradiation, drying air speed, and weight were instantly monitored and recorded. Moisture content of sliced and pretreated cantaloupe were converted into moisture ratio and then fitted against drying time for constructing drying curves. Then, 10 quasi-theoretical and empirical drying models were applied to find the best drying curve equation according to the Levenberg-Marquardt nonlinear optimization method. The best fitted mathematical drying model was selected according to the highest coefficient of determination (R²), and the mean square of the deviations (χ^²) and root mean square error (RMSE) criterial. The best fitted model was utilized to simulate a thin layer solar drying of cantaloupe, and the simulation results were compared with the experimental data for validation purposes.

Keywords: solar dryer, mathematical modelling, drying kinetics, cantaloupe drying

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Authors: Taik Kim

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Mathematics anxiety has an important consequence for teacher practices that influence students’ attitudes and achievement. Elementary prospective teachers have the highest levels of mathematics anxiety in comparison with other college majors. In his teaching practice, the researcher developed a highly successful teaching model to reduce pre-service teachers’ higher math anxiety and simultaneously to improve their pedagogical math content knowledge. There were eighty one participants from 2015 to 2018 who took the Mathematics for Elementary Teachers I and II. As the analysis data indicated, elementary prospective teachers’ math anxiety was greatly reduced with improving their math pedagogical knowledge. U.S encounters a critical shortage of well qualified educators. To solve the issue, it is essential to engage students in a long-term commitmentto shape better teachers, who will, in turn, produce k-12 school students that are better-prepared for college students. It is imperative that new instructional strategies are implemented to improve student learning and address declining interest, poor preparedness, a lack of diverse representation, and low persistence of students in mathematics. Many four year college students take math courses from the math department in the College of Arts& Science and then take methodology courses from the College of Education. Before taking pedagogy, many students struggle in learning mathematics and lose their confidence. Since the content course focus on college level math, instead of pre service teachers’ teaching area, per se elementary math, they do not have a chance to improve their teaching skills on topics which eventually they teach. The research, a joint appointment of math and math education, has been involved in teaching content and pedagogy. As the result indicated, participants were able to math content at the same time how to teach. In conclusion, the new initiative to use several teaching strategies was able not only to increase elementary prospective teachers’ mathematical skills and knowledge but also to improve their attitude toward mathematics. We need an innovative teaching strategy which implements evidence-based tactics in redesigning a education and math to improve pre service teachers’math skills and which can improve students’ attitude toward math and students’ logical and reasoning skills. Implementation of these best practices in the local school district is particularly important because K-8 teachers are not generally familiar with lab-based instruction. At the same time, local school teachers will learn a new way how to teach math. This study can be a vital teacher education model expanding throughout the State and nationwide. In summary, this study yields invaluable information how to improve teacher education in the elementary level and, eventually, how to enhance K-8 students’ math achievement.

Keywords: quality of education and improvement method, teacher education, innovative teaching and learning methodologies, math education

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Authors: Rami Al-Sadi

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The assignment of lexical stress in English to its correct syllable is an enormous challenge to EFL learners, especially if their first language (L1) phonology is very different from English phonology. Arabic-speaking EFL learners not only stumble very frequently when it comes to placing the lexical stress in a given word, but they also seem to relegate the role of lexical stress as unimportant, mainly because in Arabic, unlike in English, lexical stress is not phonemic. This study aims at exploring the possible benefits of utilizing spectrographic representations of English words correctly pronounced, for the purpose of finding out how these spectrograms can provide a visual aid to the learners and help them rectify their stress placement errors as they see in real time spectrograms of the correct pronunciations juxtaposed on a computer screen with spectrograms of their own pronunciations for easy comparison. The study involved 120 students from the English Department at Prince Sattam bin Abdulaziz University in Saudi Arabia. 60 participants were taught the English lexical stress rules and also received spectrographic guidance on pronunciation; the other 60 received only verbal instruction on the stress rules and verbal feedback on their pronunciations. Statistical results showed that when the learners had the opportunity to ‘see’ their pronunciation mistakes, they were three times more likely to rectify their placement of lexical stress.

Keywords: Arabic-speaking EFL learners, lexical stress, pronunciation, spectrographic representation, stress placement

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Authors: Mohamed Sameer Mughal, Andrew D. Ross, Damian J. Fearon

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Background: The fast-paced changing landscape of UK Higher Education Institution (HEIs) is poised by changes and challenges affecting Middle Managers (MM) in their job roles. MM contribute to the success of HEIs by balancing the equilibrium and pass organization strategies from senior staff towards operationalization directives to junior staff. However, this study showcased from the data analyzed during the semi structured interviews; MM job role is becoming more complex due to changes and challenges creating colossal pressures and workloads in day-to-day working. Current development programmes provisions by Human Resources (HR) departments in such HEIs are not feasible, applicable, and matching the true essence and requirements of MM who suggest that programmes offered by HR are too generic to suit their precise needs and require tailor made espousal to work effectively in their pertinent job roles. Methodologies: This study aims to capture demands of MM Development Needs (DN) by means of a conceptual model as conclusive part of the research that is divided into 2 phases. Phase 1 initiated by carrying out 2 pilot interviews with a retired Emeritus status professor and HR programmes development coordinator. Key themes from the pilot and literature review subsidized into formulation of 22 set of questions (Kvale and Brinkmann) in form of interviewing questionnaire during qualitative data collection. Data strategy and collection consisted of purposeful sampling of 12 semi structured interviews (n=12) lasting approximately an hour for all participants. The MM interviewed were at faculty and departmental levels which included; deans (n=2), head of departments (n=4), subject leaders (n=2), and lastly programme leaders (n=4). Participants recruitment was carried out via emails and snowballing technique. The interviews data was transcribed (verbatim) and managed using Computer Assisted Qualitative Data Analysis using Nvivo ver.11 software. Data was meticulously analyzed using Miles and Huberman inductive approach of positivistic style grounded theory, whereby key themes and categories emerged from the rich data collected. The data was precisely coded and classified into case studies (Robert Yin); with a main case study, sub cases (4 classes of MM) and embedded cases (12 individual MMs). Major Findings: An interim conceptual model emerged from analyzing the data with main concepts that included; key performance indicators (KPI’s), HEI effectiveness and outlook, practices, processes and procedures, support mechanisms, student events, rules, regulations and policies, career progression, reporting/accountability, changes and challenges, and lastly skills and attributes. Conclusion: Dynamic elements affecting MM includes; increase in government pressures, student numbers, irrelevant development programmes, bureaucratic structures, transparency and accountability, organization policies, skills sets… can only be confronted by employing structured development programmes originated by HR that are not provided generically. Future Work: Stage 2 (Quantitative method) of the study plans to validate the interim conceptual model externally through fully completed online survey questionnaire (Bram Oppenheim) from external HEIs (n=150). The total sample targeted is 1500 MM. Author contribution focuses on enhancing management theory and narrow the gap between by HR and MM development programme provision.

Keywords: development needs (DN), higher education institutions (HEIs), human resources (HR), middle managers (MM)

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Authors: Kulveer Sandhu

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Background: In 1999, the Government of Canada legalized the use of cannabis for the therapeutic purpose (CTP); however, its users remain highly vulnerable to stigma and are judged by care providers and nonusers of cannabis. Findings from a literature review suggest health care providers (HCPs), including nurses in palliative care settings, lack knowledge about medical cannabis. For this reason, it is important to enhance HCPs’awarenessand knowledge of medical cannabis. Significance of the Project: Nurses are the first point of contact and spend more time with patients than other care providers; it is, therefore, important for them to be informed about CTPto provide quality and equitable care for medical cannabis users. Although nurses and other HCPs want information on CTP, the topic is rarely included in their educational curriculum. The purpose of this project is to create an evidence informed Package designed to increase knowledge among palliative care nurses about CTP. The information package will empower palliative nurses to help palliative patients make informed decisions about their treatment plan. Method: The information package will include a basic overview of the endocannabinoid system, common cannabis plants and products, and methods of consumption, as well as information to help nurses better understand consumption and harm reduction. The package will also include a set of cannabis fact sheets for nurses. Each fact sheet will comprise a high-level overview with graphics followed by a description of medical cannabis with links and references. At the end of the learning package, there are five self-reflection questions that allow nurses to examine their personal values, attitudes, and practices regarding medical cannabis. These questions will help each nurse understand their personal approach towards CTP and its users.

Keywords: medical cannabis, improve knowledge, cannabis for therapeutic purpose (CTP), patient experience, palliative care

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Authors: Dembele Dembele, Philippe

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Several Second Language Acquisition (SLA) studies have stressed the benefits of Content and Language Integrated Learning (CLIL) and shown how CLIL students outperformed their non-CLIL counterparts in many L2 skills. However, numerous experimental CLIL programs seem to have mainly targeted above-average and rather highly motivated language learners. The need to understand the impact of the student’s language proficiency on code-switching in CLIL instruction motivated this study. Therefore, determining the implications of the students’ low-language proficiency for CLIL methodology, as well as the frequency with which CLIL teachers use the main pedagogical functions of code-switching, seemed crucial for a Spanish CLIL instruction on a large scale. In the mixed-method approach adopted, ten face-to-face interviews were conducted in nine Valencian public secondary education schools, while over 30 CLIL teachers also contributed with their experience in two online survey questionnaires. The results showed the crucial role language proficiency plays in the Valencian CLIL/Plurilingual selection criteria. The presence of a substantial number of low-language proficient students in CLIL groups, which in turn implied important methodological consequences, was another finding of the study. Indeed, though the pedagogical use of L1 was confirmed as an extended practice among CLIL teachers, more than half of the participants perceived that code-switching impaired attaining their CLIL lesson objectives. Therein, the dissertation highlights the need for more extensive empirical research on how code-switching could prove beneficial in CLIL instruction involving low-language proficient students while maintaining the maximum possible exposure to the target language.

Keywords: CLIL methodology, low language proficiency, code switching, selection criteria, code-switching functions

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Authors: Farzad Hosseini Hossein Abadi, Cristina Prieto Sierra, Cesar Álvarez Díaz

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Predicting streamflow accurately in flashy catchments prone to floods is a major research and operational challenge in hydrological modeling. Recent advancements in deep learning, particularly Long Short-Term Memory (LSTM) networks, have shown to be promising in achieving accurate hydrological predictions at daily and hourly time scales. In this work, a multi-timescale LSTM (MTS-LSTM) network was applied to the context of regional hydrological predictions at an hourly time scale in flashy catchments. The case study includes 40 catchments allocated in the Basque Country, north of Spain. We explore the impact of hyperparameters on the performance of streamflow predictions given by regional deep learning models through systematic hyperparameter tuning - where optimal regional values for different catchments are identified. The results show that predictions are highly accurate, with Nash-Sutcliffe (NSE) and Kling-Gupta (KGE) metrics values as high as 0.98 and 0.97, respectively. A principal component analysis reveals that a hyperparameter related to the length of the input sequence contributes most significantly to the prediction performance. The findings suggest that input sequence lengths have a crucial impact on the model prediction performance. Moreover, employing catchment-scale analysis reveals distinct sequence lengths for individual basins, highlighting the necessity of customizing this hyperparameter based on each catchment’s characteristics. This aligns with well known “uniqueness of the place” paradigm. In prior research, tuning the length of the input sequence of LSTMs has received limited focus in the field of streamflow prediction. Initially it was set to 365 days to capture a full annual water cycle. Later, performing limited systematic hyper-tuning using grid search, revealed a modification to 270 days. However, despite the significance of this hyperparameter in hydrological predictions, usually studies have overlooked its tuning and fixed it to 365 days. This study, employing a simultaneous systematic hyperparameter tuning approach, emphasizes the critical role of input sequence length as an influential hyperparameter in configuring LSTMs for regional streamflow prediction. Proper tuning of this hyperparameter is essential for achieving accurate hourly predictions using deep learning models.

Keywords: LSTMs, streamflow, hyperparameters, hydrology

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