The findings come from Third Sector’s inaugural Charity Employer Index

The synthesis, crystal structure, and Hirshfeld surface analysis of N-(4-methoxyphenyl)picolinamide (MPPA), C13H12N2O2, are presented. MPPA crystallizes in the monoclinic space group P21/n, with a single molecule in the asymmetric unit. Structural analysis reveals that all non-hydrogen atoms are nearly coplanar, and the molecule exhibits two intramolecular hydrogen bonds that stabilize its conformation. Supramolecular features include significant intermolecular interactions, primarily C—H...π and various hydrogen bonds, contributing to the overall crystal cohesion, as confirmed by energy framework calculations yielding a total interaction energy of −138.3 kJ mol−1. Hirshfeld surface analysis indicates that H...H interactions dominate, followed by C...H and O...H interactions, highlighting the role of van der Waals forces and hydrogen bonding in crystal packing.

Colby Fischer is an innovative leader with more than a decade of experience in the field of corporate finance

UX competitor analysis is a valuable user research method that focuses on understanding your products’ competitors, helping you better understand your market and goals. Idea Theorem™ has worked with many clients that required a UX competitor analysis to get actionable insights about their competitors’ strengths, weaknesses, and mistakes to avoid and know what they are doing right.

Fundamental Overview

The US CPI yesterday came in line with expectations leading to a bit of a “sell the fact” reaction in the US Dollar.

The bullish momentum picked up a bit later though as Fed’s Logan delivered a hawkish comment saying that “models show that Fed funds could be very close to neutral” basically implying a lot more cautious approach on rate cuts in 2025.

The market is viewing all of this in light of the recent US election as Trump’s policies are likely to spur growth and potentially keep inflation above target for longer, making the Fed’s job of bringing inflation back to target a bit harder.

USDJPY Technical Analysis – Daily Timeframe

On the daily chart, we can see that USDJPY finally extended the rally into new highs helped by a hawkish comment from Fed’s Logan. There’s no strong technical resistance now at least until the 160.00 handle.

If we get a pullback, the buyers will likely lean on the trendline with a defined risk below it to position for a rally into the 160.00 handle. The sellers, on the other hand, will want to see the price breaking lower to start targeting a drop back into the 152.00 support.

USDJPY Technical Analysis – 4 hour Timeframe

On the 4 hour chart, we can see that we have a minor upward trendline defining the current bullish momentum. If we get a pullback, the buyers will likely lean on it to position for new highs, while the sellers will look for a break lower to target a break below the major trendline.

USDJPY Technical Analysis – 1 hour Timeframe

On the 1 hour chart, there’s not much else we can add as from a risk management perspective, the buyers will have a better setup around the trendline, while the sellers are better to wait for a technical break lower instead of trying to catch the top. The red lines define the average daily range for today.

Upcoming Catalysts

Today we have the US PPI and the US Jobless Claims figures. Tomorrow, we conclude the week with the US Retail Sales data.

See the video below

Fundamental Overview

The US CPI yesterday came in line with expectations leading to a bit of a “sell the fact” reaction in the US Dollar.

The bullish momentum picked up a bit later though as Fed’s Logan delivered a hawkish comment saying that “models show that Fed funds could be very close to neutral” basically implying a lot more cautious approach on rate cuts in 2025.

The market is viewing all of this in light of the recent US election as Trump’s policies are likely to spur growth and potentially keep inflation above target for longer, making the Fed’s job of bringing inflation back to target a bit harder.

AUDUSD Technical Analysis – Daily Timeframe

On the daily chart, we can see that AUDUSD broke through the recent low around the 0.6537 level and extended the drop into the 0.6460 level as the US Dollar restarted its run on stronger US data. The natural target should be around the 0.6362 level.

From a risk management perspective, the sellers will have a better risk to reward setup around the trendline. The buyers, on the other hand, will want to see the price breaking higher to start targeting a rally into the top of the yearly range around the 0.69 handle.

AUDUSD Technical Analysis – 4 hour Timeframe

On the 4 hour chart, we can see that we have another minor downward trendline defining the current bearish momentum. If we were to get a pullback, the sellers will likely lean on the trendline with a defined risk above it to position for a drop into new lows. The buyers, on the other hand, will want to see the price breaking higher to start targeting a bigger pullback into the major trendline.

AUDUSD Technical Analysis – 1 hour Timeframe

On the 1 hour chart, there’s not much more we can add although we can see that we have a minor resistance zone around the 0.65 handle. If the price gets there, we can expect the sellers to pile in for move lower, while the buyers will look for a break higher. The red lines define the average daily range for today.

Upcoming Catalysts

Today we have the US PPI and the US Jobless Claims figures. Tomorrow, we conclude the week with the US Retail Sales data.

Saudi Arabia 5G Technology Analysis Report 2024: Market to  GlobeNewswire

The long lost remains of Thomas Kent, one of the 16 men executed in 1916 following the Easter Rising, have been identified by scientific DNA analysis...

Nov. 13, 2024 — When researchers use high-performance computing (HPC)—to model electric vehicle infrastructure or to test the performance of sustainable technologies, for example—they can see how advanced computing impacts their […]

The post NREL Partnership Demonstrates Computationally Efficient Analysis of Hybrid Plants appeared first on HPCwire.

Donald Trump was elected with a mandate last week. He's now using it to challenge Republican senators to confirm his Cabinet picks.

Israel's military campaign against Hamas and Hezbollah could reshape Middle Eastern alliances and weaken Iran's regional influence.

Indian stock markets have shown notable volatility over the past two days, with sharp declines unsettling investors and signalling potential shifts in market sentiment

Sommers, M J; Gingras, M K; MacNaughton, R B; Fallas, K M; Morgan, C A. Bulletin of Canadian Petroleum Geology vol. 68, no. 1, 2020 p. 1-29, https://doi.org/10.35767/gscpgbull.68.1.1
<a href="https://geoscan.nrcan.gc.ca/images/geoscan/20190098_329482.jpg"><img src="https://geoscan.nrcan.gc.ca/images/geoscan/20190098_329482.jpg" title="Bulletin of Canadian Petroleum Geology vol. 68, no. 1, 2020 p. 1-29, https://doi.org/10.35767/gscpgbull.68.1.1" height="150" border="1" /></a>

Dafoe, L T; Williams, G L. Geological Survey of Canada, Open File 8670, 2020, 103 pages (10 sheets), https://doi.org/10.4095/321502
<a href="https://geoscan.nrcan.gc.ca/images/geoscan/of8670.jpg"><img src="https://geoscan.nrcan.gc.ca/images/geoscan/of8670.jpg" title="Geological Survey of Canada, Open File 8670, 2020, 103 pages (10 sheets), https://doi.org/10.4095/321502" height="150" border="1" /></a>

Rice, J M; Ross, M; Paulen, R C; Kelley, S E; Briner, J P. Earth Surface Processes and Landforms 2020 p. 1-23, https://doi.org/10.1002/esp.4957
<a href="https://geoscan.nrcan.gc.ca/images/geoscan/20190357.jpg"><img src="https://geoscan.nrcan.gc.ca/images/geoscan/20190357.jpg" title="Earth Surface Processes and Landforms 2020 p. 1-23, https://doi.org/10.1002/esp.4957" height="150" border="1" /></a>

Dafoe, L T; Williams, G L. Geological Survey of Canada, Bulletin 613, 2020, 146 pages, https://doi.org/10.4095/315362
<a href="https://geoscan.nrcan.gc.ca/images/geoscan/gid_315362.jpg"><img src="https://geoscan.nrcan.gc.ca/images/geoscan/gid_315362.jpg" title="Geological Survey of Canada, Bulletin 613, 2020, 146 pages, https://doi.org/10.4095/315362" height="150" border="1" /></a>

de Bronac de Vazelhes, V; McMartin, I; Côté-Mantha, O; Boulianne-Verschelden, N. Journal of Geochemical Exploration vol. 228, 106800, 2021 p. 1-17, https://doi.org/10.1016/j.gexplo.2021.106800
<a href="https://geoscan.nrcan.gc.ca/images/geoscan/20200684.jpg"><img src="https://geoscan.nrcan.gc.ca/images/geoscan/20200684.jpg" title="Journal of Geochemical Exploration vol. 228, 106800, 2021 p. 1-17, https://doi.org/10.1016/j.gexplo.2021.106800" height="150" border="1" /></a>

Dafoe, L T; Dickie, K; Williams, G L. GAC-MAC-IAH-CNC-CSPG, Halifax 2022; 2022 p. 79
<a href="https://geoscan.nrcan.gc.ca/images/geoscan/20210585.jpg"><img src="https://geoscan.nrcan.gc.ca/images/geoscan/20210585.jpg" title="GAC-MAC-IAH-CNC-CSPG, Halifax 2022; 2022 p. 79" height="150" border="1" /></a>

Berman, R G; Taylor, B E; Davis, W J; Sanborn-Barrie, M; Whalen, J B. Canada's northern Shield: new perspectives from the Geoscience for Energy and Minerals Program; Geological Survey of Canada, Bulletin 612, 2024 p. 115-158, https://doi.org/10.4095/332497
<a href="https://geoscan.nrcan.gc.ca/images/geoscan/gid_332497.jpg"><img src="https://geoscan.nrcan.gc.ca/images/geoscan/gid_332497.jpg" title="Canada's northern Shield: new perspectives from the Geoscience for Energy and Minerals Program; Geological Survey of Canada, Bulletin 612, 2024 p. 115-158, https://doi.org/10.4095/332497" height="150" border="1" /></a>

Background

Asthma exacerbations in children pose a significant burden on healthcare systems and families. While traditional risk assessment tools exist, artificial intelligence (AI) offers the potential for enhanced prediction models.

Background

Lung cancer ranks as the leading cause of cancer-related deaths worldwide. There is evidence that second-hand smoke (SHS) exposure is a risk factor for the development of lung cancer in never-smokers. This systematic review and meta-analysis aims to provide the most accurate quantification of the association between SHS exposure and lung cancer risk in never-smokers.

(EMAILWIRE.COM, October 28, 2024 ) Grain Analysis Market overview Grain Analysis Market refers to the process of evaluating grain for impurities like mycotoxins and pesticide residue, as well as quality and safety, during distribution, storage to minimize waste. Grain Analysis Market drivers Increased...

(EMAILWIRE.COM, October 30, 2024 ) The global ammunition market size is projected to grow from USD 28.0 billion in 2023 to USD 33.1 billion in 2028, at a CAGR of 3.4% from 2023 to 2028. The factors such as the increase in the geopolitical tensions, Growth in military expenditure and arms transfer,...

(EMAILWIRE.COM, November 08, 2024 ) The Military Platforms Market will grow tremendously from 2020 through 2030, based on the increase in defense spending, escalations in geopolitical tensions, and needs for advanced border security and early warning systems The military platforms market is projected...

(EMAILWIRE.COM, November 08, 2024 ) The global aerospace testing market was valued at USD 5.29 billion in 2024 and is projected to reach USD 6.68 billion by 2029; it is expected to register a CAGR of 4.8% during the forecast period. Increasing air travel across the globe has increased the demand...

As per the research report titled Global Podcasting Market Size study, by Genre, by Format (Interviews, Panels, Solo, Conversational) and Regional Forecasts 2020-2027 available with Market Study Report LLC, global podcasting market is expected to witness unprecedented growth during 2020-2027.

According to the business intelligence report, emphasis among podcast production studios on the distribution and production of their content on audio platforms such as Spotify, coupled with emergence of high bandwidth, and personal digital assistants are augmenting the growth of global podcasting market size.

Increasing penetration of internet as well as smartphones, inclination towards audio and music content, growing acceptance of audio broadcasting content, and escalating demand for podcasts are stimulating the global podcasting market outlook. Citing an instance, the IDC (International Data Corporation) recorded shipment of around 369.8 million units of smartphones by vendors in the fourth quarter of 2019.

Leading players that define global podcasting industry trends are TuneIn Inc., Stitcher Radio, Spotify AB, SoundCloud, Entercom Communications Corporation, Pandora Media LLC, Megaphone LLC, iHeartMedia Inc., and Apple Inc.

On the contrary, storage space issues and high costs associated with podcasting are expected to impede the industry expansion throughout the analysis timeframe.

Collaborative Filtering(CF) is a well-known technique in recommender systems. CF exploits relationships between users and recommends items to the active user according to the ratings of his/her neighbors. CF suffers from the data sparsity problem, where users only rate a small set of items. That makes the computation of similarity between users imprecise and consequently reduces the accuracy of CF algorithms. In this article, we propose a clustering approach based on the social information of users to derive the recommendations. We study the application of this approach in two application scenarios: academic venue recommendation based on collaboration information and trust-based recommendation. Using the data from DBLP digital library and Epinion, the evaluation shows that our clustering technique based CF performs better than traditional CF algorithms.

This paper aims to analyse the effectiveness of cost control oriented enterprise financial risk management. Firstly, it analyses the importance of enterprise financial risk management. Secondly, the position of cost control in enterprise financial risk management was analysed. Cost control can be used to reduce the operating costs of enterprises, improve their profitability, and thus reduce the financial risks they face. Finally, a corporate financial risk management strategy is constructed from several aspects: establishing a sound risk management system, predicting and responding to various risks, optimising fund operation management, strengthening internal control, and enhancing employee risk awareness. The results show that after applying the proposed management strategy, the enterprise performs well in cost control oriented enterprise financial risk management, with a cost accounting accuracy of 95% and an audit system completeness of 90%. It can also help the enterprise develop emergency plans and provide comprehensive risk management strategy coverage.

The current study has outlined slow fashion (SF) research trends and created a future research agenda for this field. It is a thorough analysis of the literature on slow fashion. Numerous bibliometric features of slow fashion have been discussed in the paper. This study comprises 182 research articles from the Scopus database. The database was utilised for bibliometric analysis. To identify certain trends in the area of slow fashion, a bibliometric study is done. For bibliometric analysis, the study employed R-software (the Biblioshiny package). Here, VOSviewer software is used to determine the co-occurrence of authors, countries, sources, etc. The study has outlined the gap that still exists in the field of slow fashion. Here, the research outcome strengthens the domain of slow fashion for sustainable consumption. The study findings will be useful for policymakers, industry professionals, and researchers.

This systematic literature study employed bibliometric analysis to identify workplace mindfulness-related methods and practices in literature published from 2000 to 2020 by leading nations, institutions, journals, authors, and keywords. We also assessed the impact of workplace mindfulness research papers. Scopus analysis tools provided a literature report for 638 Scopus articles used in the study. Using VOSviewer, leading nations, institutions, articles, authors, journals, and keyword co-occurrence network maps were constructed. PRISMA was used to identify 56 publications to recognise workplace mindfulness literature's significant achievements. The research's main contribution is a deep review of neurological mindfulness and psychological measuring tools as workplace mindfulness tool categories. The study is the first to use the PRISMA technique to capture the essential contributions of workplace mindfulness papers from 2000 to 2020.

Although artificial intelligence provides a new method to gather, process, analyse data, generate insights, and offer customised solutions, such methods could change how marketers deal with customers, and there is a lack of literature to portray the application of artificial intelligence in marketing. This study aims to recognise and portray the use of artificial intelligence from a marketing standpoint, as well as to provide a conceptual framework for the application of artificial intelligence in marketing. This study uses a systematic literature review analysis as a research method to achieve the aims. Data from 142 articles were extracted from the Scopus database using relevant search terms for artificial intelligence and marketing. The systematic review identified significant usage of artificial intelligence in conversational artificial intelligence, content creation, audience segmentation, predictive analytics, personalisation, paid ads, sales forecasting, dynamic pricing, and recommendation engines and the bibliometric analysis produced the trend in co-authorship, citation, bibliographic coupling, and co-citation analysis. Practitioners and academics may use this study to decide on the marketing area in which artificial intelligence can be invested and used.

It is inevitable for basketball players to have physical injury in sports. Reducing basketball injury is one of the main aims of the study of basketball. In view of this, this paper proposes a monocular vision and FMS injury prediction model for basketball players. Aiming at the limitations of traditional FMS testing methods, this study introduces intelligent machine learning methods. In this study, random forest algorithm was introduced into OpenPose network to improve model node occlusion, missed detection or false detection. In addition, to reduce the computational load of the network, the original OpenPose network was replaced by a lightweight OpenPose network. The experimental results show that the average processing time of the proposed model is about 90 ms, and the output video frame rate is 10 frames per second, which can meet the real-time requirements. This study analysed the students participating in the basketball league of the College of Sports Science of Nantong University, and the results confirmed the accuracy of the injury prediction of college basketball players based on FMS scores. It is hoped that this study can provide some reference for the research of injury prevention of basketball players.

Along with amazing advancements in the field of digital payments, this article seeks to provide a summary of research undertaken over the last four decades and to suggest areas in need of additional study. This study employs a two-pronged technique for analysing its data. The first is concerned with performance analysis, and the second with science mapping. The study uses the apps VOS viewer and R-studio to do bibliometric data analysis. From 1982 until May 2022, the most trustworthy database, Scopus, is used to compile a database of 923 publications The findings of this study identify the scope of current research interest, which is explored with critical contributions from a variety of authors, journals, countries, affiliations, keyword analysis, citation analysis, co-citation analysis, and bibliometric coupling, as well as a potential research direction for further investigation in this emerging field.

The proliferation of technology and communication has resulted in increased digitalisation that includes digital payments. This study is aimed at unravelling the relationship between awareness of individuals about the digital payment system and customer satisfaction with digital payments. Two models were developed in this study. First model considers awareness → usage pattern → customer satisfaction. Second model considers usage pattern → customer satisfaction → perception of digital payments. These two alternative models were tested by collecting data from 507 respondents in southern India was analysed using the structural equation modelling. The results indicate that usage pattern acted as a mediator between awareness and satisfaction, and satisfaction acted as a mediator between usage pattern and consumers' perception of digital payments. The implications for theory and practice are discussed.

Emergency disaster management presents substantial risks and obstacles to shield tunnel building projects, particularly in the event of water leakage accidents. Contemporary water leak detection is critical for guaranteeing safety by reducing the likelihood of disasters and the severity of any resulting damages. However, it can be difficult. Deep learning models can analyse images taken inside the tunnel to look for signs of water damage. This study introduces a unique strategy that employs deep learning techniques, generative adversarial networks (GAN) with long short-term memory (LSTM) for water leakage detection i shield tunnel construction (WLD-STC) to conduct classification and prediction tasks on the massive image dataset. The results demonstrate that for identifying and analysing water leakage episodes during shield tunnel construction, the WLD-STC strategy using LSTM-based GAN networks outperformed other methods, particularly on huge data.

To achieve psychological and emotional analysis of massive internet chats, researchers have used statistical methods, machine learning, and neural networks to analyse the dynamic tendencies of texts dynamically. For long readers, the author first compares and explores the differences between the two psychoanalysis algorithms based on the emotion dictionary and machine learning for simple sentences, then studies the expansion algorithm of the emotion dictionary, and finally proposes an extended text psychoanalysis algorithm based on conditional random field. According to the experimental results, the mental dictionary's accuracy, recall, and F-score based on the cognitive understanding of each additional ten words were calculated. The optimisation decreased, and the memory and F-score improved. An <i>F</i>-value greater than 1, which is the most effective indicator for evaluating the effectiveness of a mental analysis problem, can better demonstrate that the algorithm is adaptive in the literature dictionary. It has been proven that this scheme can achieve good results in analysing emotional tendencies and has higher efficiency than ordinary weight-based psychological sentiment analysis algorithms.

The research uses a machine learning approach to appraising the validity of customer aptness for a loan. Banks and non-banking financial companies (NBFC) face significant non-performing assets (NPAs) threats because of the non-payment of loans. In this study, the data is collected from Kaggle and tested using various machine learning models to determine if the borrower can repay its loan. In addition, we analysed the performance of the models [K-nearest neighbours (K-NN), logistic regression, support vector machines (SVM), decision tree, naive Bayes and neural networks]. The purpose is to support decisions that are based not on subjective aspects but objective data analysis. This work aims to analyse how objective factors influence borrowers to default loans, identify the leading causes contributing to a borrower's default loan. The results show that the decision tree classifier gives the best result, with a recall rate of 0.0885 and a false- negative rate of 5.4%.

The face of the hospitality sector in India is continuously changing and in times of career transitiveness, it is important to know the factors that support a successful career. The current research aims to explore the relationship between career planning, employee optimism, career adaptability and career satisfaction in the Indian hospitality sector. The study included 283 employees from Indian hospitality sector. Additionally, the study used SEM and bootstrap method to measure the dual mediating relationship between career planning, employee optimism dimensions, career adaptability dimensions, and career satisfaction in Indian setting. The results indicated that optimism dimensions and career adaptability dimensions partially mediate the relationship between career planning and career satisfaction in Indian hospitality sector. The study suggests useful implications for academia and industrial purpose. The limitations and future research avenues have been discussed. The study would contribute to the sparse literature on employee optimism, career planning, career adaptability and subjective career success. It would contribute to the social cognitive career theory (SCCT).

Aim/Purpose: From an idea of lifelong-learning-for-all to a phenomenon affecting higher education, Massive Open Online Courses (MOOCs) can be the next step to a truly universal education. Indeed, MOOC enrolment rates can be astoundingly high; still, their completion rates are frequently disappointingly low. Nevertheless, as courses, the participants’ enrolment and learning within the MOOCs must be considered when assessing their success. In this paper, the authors’ aim is to reflect on what makes a MOOC successful to propose an analysis framework of MOOC success factors. Background: A literature review was conducted to identify reported MOOC success factors and to propose an analysis framework. Methodology: This literature-based framework was tested against data of a specific MOOC and refined, within a qualitative interpretivist methodology. The data were collected from the ‘As alterações climáticas nos média escolares - Clima@EduMedia’ course, which was developed by the project Clima@EduMedia and was submitted to content analysis. This MOOC aimed to support science and school media teachers in the use of media to teach climate change Contribution: By proposing a MOOC success factors framework the authors are attempting to contribute to fill in a literature gap regarding what concerns criteria to consider a specific MOOC successful. Findings: This work major finding is a literature-based and empirically-refined MOOC success factors analysis framework. Recommendations for Practitioners: The proposed framework is also a set of best practices relevant to MOOC developers, particularly when targeting teachers as potential participants. Recommendation for Researchers: This work’s relevance is also based on its contribution to increasing empirical research on MOOCs. Impact on Society: By providing a proposal of a framework on factors to make a MOOC successful, the authors hope to contribute to the quality of MOOCs. Future Research: Future work should refine further the proposed framework, by in testing it against data collected in other MOOCs.

Aim/Purpose: The study examined types of errors made by novice programmers in different Java concepts with students of different ability levels in programming as well as the perceived causes of such errors. Background: To improve code writing and debugging skills, efforts have been made to taxonomize programming errors and their causes. However, most of the studies employed omnibus approaches, i.e. without consideration of different programing concepts and ability levels of the trainee programmers. Such concepts and ability specific errors identification and classifications are needed to advance appropriate intervention strategy. Methodology: A sequential exploratory mixed method design was adopted. The sample was an intact class of 124 Computer Science and Engineering undergraduate students grouped into three achievement levels based on first semester performance in a Java programming course. The submitted codes in the course of second semester exercises were analyzed for possible errors, categorized and grouped across achievement level. The resulting data were analyzed using descriptive statistics as well as Pearson product correlation coefficient. Qualitative analyses through interviews and focused group discussion (FGD) were also employed to identify reasons for the committed errors. Contribution:The study provides a useful concept-based and achievement level specific error log for the teaching of Java programming for beginners. Findings: The results identified 598 errors with Missing symbols (33%) and Invalid symbols (12%) constituting the highest and least committed errors respec-tively. Method and Classes concept houses the highest number of errors (36%) followed by Other Object Concepts (34%), Decision Making (29%), and Looping (10%). Similar error types were found across ability levels. A significant relationship was found between missing symbols and each of Invalid symbols and Inappropriate Naming. Errors made in Methods and Classes were also found to significantly predict that of Other Object concepts. Recommendations for Practitioners: To promote better classroom practice in the teaching of Java programming, findings for the study suggests instructions to students should be based on achievement level. In addition to this, learning Java programming should be done with an unintelligent editor. Recommendations for Researchers: Research could examine logic or semantic errors among novice programmers as the errors analyzed in this study focus mainly on syntactic ones. Impact on Society: The digital age is code-driven, thus error analysis in programming instruction will enhance programming ability, which will ultimately transform novice programmers into experts, particularly in developing countries where most of the software in use is imported. Future Research: Researchers could look beyond novice or beginner programmers as codes written by intermediate or even advanced programmers are still not often completely error free.

Aim/Purpose: The study focused on learner sentiments and experiences after using the Moodle assessment module and trained a machine learning classifier for future sentiment predictions. Background: Learner assessment is one of the standard methods instructors use to measure students’ performance and ascertain successful teaching objectives. In pedagogical design, assessment planning is vital in lesson content planning to the extent that curriculum designers and instructors primarily think like assessors. Assessment aids students in redefining their understanding of a subject and serves as the basis for more profound research in that particular subject. Positive results from an evaluation also motivate learners and provide employment directions to the students. Assessment results guide not just the students but also the instructor. Methodology: A modified methodology was used for carrying out the study. The revised methodology is divided into two major parts: the text-processing phase and the classification model phase. The text-processing phase consists of stages including cleaning, tokenization, and stop words removal, while the classification model phase consists of dataset training using a sentiment analyser, a polarity classification model and a prediction validation model. The text-processing phase of the referenced methodology did not utilise tokenization and stop words. In addition, the classification model did not include a sentiment analyser. Contribution: The reviewed literature reveals two major omissions: sentiment responses on using the Moodle for online assessment, particularly in developing countries with unstable internet connectivity, have not been investigated, and variations of the k-fold cross-validation technique in detecting overfitting and developing a reliable classifier have been largely neglected. In this study we built a Sentiment Analyser for Learner Emotion Management using the Moodle for assessment with data collected from a Ghanaian tertiary institution and developed a classification model for future sentiment predictions by evaluating the 10-fold and the 5-fold techniques on prediction accuracy. Findings: After training and testing, the RF algorithm emerged as the best classifier using the 5-fold cross-validation technique with an accuracy of 64.9%. Recommendations for Practitioners: Instead of a closed-ended questionnaire for learner feedback assessment, the open-ended mechanism should be utilised since learners can freely express their emotions devoid of restrictions. Recommendation for Researchers: Feature selection for sentiment analysis does not always improve the overall accuracy for the classification model. The traditional machine learning algorithms should always be compared to either the ensemble or the deep learning algorithms Impact on Society: Understanding learners’ emotions without restriction is important in the educational process. The pedagogical implementation of lessons and assessment should focus on machine learning integration Future Research: To compare ensemble and deep learning algorithms