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Fulltext A research review: how technology helps to improve the learning process of learners with dyslexia

Chai JT, Chen CJ

Journal of Cognitive Sciences & Human Development, 2017;2(2):26-43.
MyJurnal

Dyslexia is a language disorder that leads to difficulty with words and it is the most common type of learning disability. This article presents a systematic review on the current state of assistive technologies used in improving the learning process of learn-ers with dyslexia. A total of 25 journals articles and international conference papers published between 2000 and 2014 were included in the review. The research articles were collected from 12 databases and analyzed based on the qualitative cyclical pro-cess. A majority of the studies focused on children and adolescents. Four main themes on the types of technologies used in aiding the learning process of learners with dys-lexia are derived and discussed. These include text-to-speech, eye-tracking, virtual learning environments, and games. The text-to-speech technology is the most common type of technology used by learners with dyslexia. In terms of the roles played by the assistive technologies, another four emerging themes are identified, which cover the roles of aiding reading, writing, memory, and mathematics. The review also discovers that a majority of these studies focus on the use of technologies for improving the reading ability of learners with dyslexia.

Matched MeSH terms: Learning; Learning Disorders
Fulltext Automatic Annotation of Unlabeled Data from Smartphone-Based Motion and Location Sensors

Pius Owoh N, Mahinderjit Singh M, Zaaba ZF

Sensors (Basel), 2018 Jul 03;18(7).
PMID: 29970823 DOI: 10.3390/s18072134

Automatic data annotation eliminates most of the challenges we faced due to the manual methods of annotating sensor data. It significantly improves users’ experience during sensing activities since their active involvement in the labeling process is reduced. An unsupervised learning technique such as clustering can be used to automatically annotate sensor data. However, the lingering issue with clustering is the validation of generated clusters. In this paper, we adopted the k-means clustering algorithm for annotating unlabeled sensor data for the purpose of detecting sensitive location information of mobile crowd sensing users. Furthermore, we proposed a cluster validation index for the k-means algorithm, which is based on Multiple Pair-Frequency. Thereafter, we trained three classifiers (Support Vector Machine, K-Nearest Neighbor, and Naïve Bayes) using cluster labels generated from the k-means clustering algorithm. The accuracy, precision, and recall of these classifiers were evaluated during the classification of “non-sensitive” and “sensitive” data from motion and location sensors. Very high accuracy scores were recorded from Support Vector Machine and K-Nearest Neighbor classifiers while a fairly high accuracy score was recorded from the Naïve Bayes classifier. With the hybridized machine learning (unsupervised and supervised) technique presented in this paper, unlabeled sensor data was automatically annotated and then classified.

Matched MeSH terms: Machine Learning; Unsupervised Machine Learning
A soft computing approach for diabetes disease classification

Nilashi M, Bin Ibrahim O, Mardani A, Ahani A, Jusoh A

Health Informatics J, 2018 12;24(4):379-393.
PMID: 30376769 DOI: 10.1177/1460458216675500

As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noise removal and classification approaches. Accordingly, we use expectation maximization, principal component analysis and support vector machine for clustering, noise removal and classification tasks, respectively. We also develop the proposed method for incremental situation by applying the incremental principal component analysis and incremental support vector machine for incremental learning of data. Experimental results on Pima Indian Diabetes dataset show that proposed method remarkably improves the accuracy of prediction and reduces computation time in relation to the non-incremental approaches. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system.

Matched MeSH terms: Machine Learning*
Reflection of physiotherapy students in clinical placement: a qualitative study

Ayiesah Ramli, Aida Safra Ruslan, Noor Shahida Sukiman

Sains Malaysiana, 2012;41:787-793.

Clinical education provides students with opportunities to integrate knowledge and skills at progressively higher levels of performance. This study determined the significant events that undergraduate physiotherapy student reflects on during their clinical experiences as they learn to become a physiotherapist. A qualitative study using reflective instruments of structured debriefing sessions and diary writing was carried out. This involves 25 fourth-year students from the Faculty
of Health Sciences, UKM during their 12 weeks of clinical placements in 3 different modules (first semester). They were required to describe an event, its value and their reaction to it, and to discuss the effect of the new learning experience and how it would influence their respond in the future. Our findings confirmed that the process of writing a diary makes a considerable impact to the student experience during clinical placement. The subjects begin to construct a personal identity of becoming a physiotherapist through the process of developing confidence, confirmation of practices and assimilating of knowledge. In conclusion, the main themes generated from a reflective diary included their reflection of personal growth, on how they learnt in a clinical setting, and on the ethical and professional behaviors of themselves and colleagues. This provides the clinical educators with valuable information to design meaningful clinical learning experiences that would assist students to become a good physiotherapist for the future.

Matched MeSH terms: Learning; Problem-Based Learning
Fulltext A Malaysian version of learning space preferences: a validation study

Sim JH, Foong CC, Pallath V, Hong WH, Vadivelu J

Int J Med Educ, 2021 May 27;12:86-93.
PMID: 34049286 DOI: 10.5116/ijme.6082.7c41

Objectives: This study aimed to validate a Malaysian version of a revised learning space questionnaire, as well as to test the utility of the revised questionnaire as a tool to investigate learning space preferences in a Malaysian medical school.
Methods: This is a cross-sectional survey. A convenient sample of 310 preclinical students of a public medical school in Malaysia were invited to participate. Validation data were collected using a revised 40-item, 5-point Likert scale learning space questionnaire. The questionnaires were administered online via a student e-learning platform. Data analysis was conducted using IBM SPSS version 24. Exploratory factor analysis was conducted to examine the factor structure of the revised questionnaire to provide evidence for construct validity. To assess the internal consistency of the revised questionnaire, Cronbach's alpha coefficients (α) were computed across all the items as well as for items within each of the factor.
Results: A total of 223 (71.94%) preclinical students completed and returned the questionnaire. In the final analysis, exploratory factor analysis with principal axis factoring and an oblimin rotation identified a six-factor, 20-item factor solution. Reliability analysis reported good internal consistency for the revised questionnaire, with an overall Cronbach's alpha of 0.845, and Cronbach's alpha ranging from 0.800 to 0.925 for the six factors.
Conclusions: This study established evidence for the construct validity and internal consistency of the revised questionnaire. The revised questionnaire appears to have utility as an instrument to investigate learning space preferences in Malaysian medical schools.

Matched MeSH terms: Learning*
Perspective of students in the Global Citizenship in Dentistry as an international peer learning experience

Yiu FSY, Yu OY, Wong AWY, Chu CH

J Dent Educ, 2021 Nov;85(11):1721-1728.
PMID: 34184258 DOI: 10.1002/jdd.12733

OBJECTIVE: To explore the achievement and perception of dental students in an international peer learning setting via the Global Citizenship in Dentistry (GCD) program.
METHODS: In the GCD program, year-2 dental students from universities in Egypt, Hong Kong, Malaysia, UK, and the United States developed a portfolio of a restorative procedure in simulation laboratory and uploaded to an online platform (https://gcd.hku.hk/). Through the platform, the students left comments on each other's portfolios to share and discuss their knowledge and experiences on restorative dentistry. This study invited students from Hong Kong in 2018-2019 to complete an open-ended questionnaire to explore their experience on the GCD program. The feedback was compiled and analyzed.
RESULTS: All 71 year-2 students completed the questionnaire. Their most dominant comments were positive feelings about learning different clinical principles and methods from universities abroad. The students also enjoyed the cultural exchange from the comfort of their own devices. Other recurrent comments included the improvement of the skills of communication and comments on the peers' work in a professional manner. The students were enthusiastic about being able to apply their critical thinking in evaluating their work. They shared their learning barriers, including the extra time needed for the program, some unenthusiastic responses from groupmates, and delayed replies from peers. They made suggestions to remove the barriers in the learning process of the GCD program.
CONCLUSION: Students generally welcomed the GCD program and benefitted from the global academic exchange, development of critical thinking, enhancing professional communication skills, as well as opportunities of cultural exchange.

Matched MeSH terms: Learning*
Kernel Bayesian ART and ARTMAP

Masuyama N, Loo CK, Dawood F

Neural Netw, 2018 Feb;98:76-86.
PMID: 29202265 DOI: 10.1016/j.neunet.2017.11.003

Adaptive Resonance Theory (ART) is one of the successful approaches to resolving "the plasticity-stability dilemma" in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state of art model is Bayesian based one, while solving the drawbacks of others. However, it is known that the Bayesian approach for the high dimensional and a large number of data requires high computational cost, and the covariance matrix in likelihood becomes unstable. This paper introduces Kernel Bayesian ART (KBA) and ARTMAP (KBAM) by integrating Kernel Bayes' Rule (KBR) and Correntropy Induced Metric (CIM) to Bayesian ART (BA) and ARTMAP (BAM), respectively, while maintaining the properties of BA and BAM. The kernel frameworks in KBA and KBAM are able to avoid the curse of dimensionality. In addition, the covariance-free Bayesian computation by KBR provides the efficient and stable computational capability to KBA and KBAM. Furthermore, Correntropy-based similarity measurement allows improving the noise reduction ability even in the high dimensional space. The simulation experiments show that KBA performs an outstanding self-organizing capability than BA, and KBAM provides the superior classification ability than BAM, respectively.

Matched MeSH terms: Machine Learning/standards*
Fulltext Testing students' e-learning via Facebook through Bayesian structural equation modeling

Salarzadeh Jenatabadi H, Moghavvemi S, Wan Mohamed Radzi CWJB, Babashamsi P, Arashi M

PLoS One, 2017;12(9):e0182311.
PMID: 28886019 DOI: 10.1371/journal.pone.0182311

Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

Matched MeSH terms: Learning*
Fulltext Constraints on Tone Sensitivity in Novel Word Learning by Monolingual and Bilingual Infants: Tone Properties Are More Influential than Tone Familiarity

Burnham D, Singh L, Mattock K, Woo PJ, Kalashnikova M

Front Psychol, 2017;8:2190.
PMID: 29354077 DOI: 10.3389/fpsyg.2017.02190

This study compared tone sensitivity in monolingual and bilingual infants in a novel word learning task. Tone language learning infants (Experiment 1, Mandarin monolingual; Experiment 2, Mandarin-English bilingual) were tested with Mandarin (native) or Thai (non-native) lexical tone pairs which contrasted static vs. dynamic (high vs. rising) tones or dynamic vs. dynamic (rising vs. falling) tones. Non-tone language, English-learning infants (Experiment 3) were tested on English intonational contrasts or the Mandarin or Thai tone contrasts. Monolingual Mandarin language infants were able to bind tones to novel words for the Mandarin High-Rising contrast, but not for the Mandarin Rising-Falling contrast; and they were insensitive to both the High-Rising and the Rising-Falling tone contrasts in Thai. Bilingual English-Mandarin infants were similar to the Mandarin monolinguals in that they were sensitive to the Mandarin High-Rising contrast and not to the Mandarin Rising-Falling contrast. However, unlike the Mandarin monolinguals, they were also sensitive to the High Rising contrast in Thai. Monolingual English learning infants were insensitive to all three types of contrasts (Mandarin, Thai, English), although they did respond differentially to tone-bearing vs. intonation-marked words. Findings suggest that infants' sensitivity to tones in word learning contexts depends heavily on tone properties, and that this influence is, in some cases, stronger than effects of language familiarity. Moreover, bilingual infants demonstrated greater phonological flexibility in tone interpretation.

Matched MeSH terms: Learning; Verbal Learning
Fulltext Extreme learning machine based optimal embedding location finder for image steganography

Atee HA, Ahmad R, Noor NM, Rahma AM, Aljeroudi Y

PLoS One, 2017;12(2):e0170329.
PMID: 28196080 DOI: 10.1371/journal.pone.0170329

In image steganography, determining the optimum location for embedding the secret message precisely with minimum distortion of the host medium remains a challenging issue. Yet, an effective approach for the selection of the best embedding location with least deformation is far from being achieved. To attain this goal, we propose a novel approach for image steganography with high-performance, where extreme learning machine (ELM) algorithm is modified to create a supervised mathematical model. This ELM is first trained on a part of an image or any host medium before being tested in the regression mode. This allowed us to choose the optimal location for embedding the message with best values of the predicted evaluation metrics. Contrast, homogeneity, and other texture features are used for training on a new metric. Furthermore, the developed ELM is exploited for counter over-fitting while training. The performance of the proposed steganography approach is evaluated by computing the correlation, structural similarity (SSIM) index, fusion matrices, and mean square error (MSE). The modified ELM is found to outperform the existing approaches in terms of imperceptibility. Excellent features of the experimental results demonstrate that the proposed steganographic approach is greatly proficient for preserving the visual information of an image. An improvement in the imperceptibility as much as 28% is achieved compared to the existing state of the art methods.

Matched MeSH terms: Machine Learning*
Fulltext Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data

Kasaraneni PP, Venkata Pavan Kumar Y, Moganti GLK, Kannan R

Sensors (Basel), 2022 Nov 30;22(23).
PMID: 36502025 DOI: 10.3390/s22239323

Addressing data anomalies (e.g., garbage data, outliers, redundant data, and missing data) plays a vital role in performing accurate analytics (billing, forecasting, load profiling, etc.) on smart homes' energy consumption data. From the literature, it has been identified that the data imputation with machine learning (ML)-based single-classifier approaches are used to address data quality issues. However, these approaches are not effective to address the hidden issues of smart home energy consumption data due to the presence of a variety of anomalies. Hence, this paper proposes ML-based ensemble classifiers using random forest (RF), support vector machine (SVM), decision tree (DT), naive Bayes, K-nearest neighbor, and neural networks to handle all the possible anomalies in smart home energy consumption data. The proposed approach initially identifies all anomalies and removes them, and then imputes this removed/missing information. The entire implementation consists of four parts. Part 1 presents anomaly detection and removal, part 2 presents data imputation, part 3 presents single-classifier approaches, and part 4 presents ensemble classifiers approaches. To assess the classifiers' performance, various metrics, namely, accuracy, precision, recall/sensitivity, specificity, and F1 score are computed. From these metrics, it is identified that the ensemble classifier "RF+SVM+DT" has shown superior performance over the conventional single classifiers as well the other ensemble classifiers for anomaly handling.

Matched MeSH terms: Machine Learning*
Analysis of the performance, emission and combustion characteristics of a turbocharged diesel engine fuelled with Jatropha curcas biodiesel-diesel blends using kernel-based extreme learning machine

Silitonga AS, Hassan MH, Ong HC, Kusumo F

Environ Sci Pollut Res Int, 2017 Nov;24(32):25383-25405.
PMID: 28932948 DOI: 10.1007/s11356-017-0141-9

The purpose of this study is to investigate the performance, emission and combustion characteristics of a four-cylinder common-rail turbocharged diesel engine fuelled with Jatropha curcas biodiesel-diesel blends. A kernel-based extreme learning machine (KELM) model is developed in this study using MATLAB software in order to predict the performance, combustion and emission characteristics of the engine. To acquire the data for training and testing the KELM model, the engine speed was selected as the input parameter, whereas the performance, exhaust emissions and combustion characteristics were chosen as the output parameters of the KELM model. The performance, emissions and combustion characteristics predicted by the KELM model were validated by comparing the predicted data with the experimental data. The results show that the coefficient of determination of the parameters is within a range of 0.9805-0.9991 for both the KELM model and the experimental data. The mean absolute percentage error is within a range of 0.1259-2.3838. This study shows that KELM modelling is a useful technique in biodiesel production since it facilitates scientists and researchers to predict the performance, exhaust emissions and combustion characteristics of internal combustion engines with high accuracy.

Matched MeSH terms: Machine Learning*
The accuracy of an Online Sequential Extreme Learning Machine in detecting voice pathology using the Malaysian Voice Pathology Database

Za'im NAN, Al-Dhief FT, Azman M, Alsemawi MRM, Abdul Latiff NMA, Mat Baki M

J Otolaryngol Head Neck Surg, 2023 Sep 20;52(1):62.
PMID: 37730624 DOI: 10.1186/s40463-023-00661-6

BACKGROUND: A multidimensional voice quality assessment is recommended for all patients with dysphonia, which requires a patient visit to the otolaryngology clinic. The aim of this study was to determine the accuracy of an online artificial intelligence classifier, the Online Sequential Extreme Learning Machine (OSELM), in detecting voice pathology. In this study, a Malaysian Voice Pathology Database (MVPD), which is the first Malaysian voice database, was created and tested.
METHODS: The study included 382 participants (252 normal voices and 130 dysphonic voices) in the proposed database MVPD. Complete data were obtained for both groups, including voice samples, laryngostroboscopy videos, and acoustic analysis. The diagnoses of patients with dysphonia were obtained. Each voice sample was anonymized using a code that was specific to each individual and stored in the MVPD. These voice samples were used to train and test the proposed OSELM algorithm. The performance of OSELM was evaluated and compared with other classifiers in terms of the accuracy, sensitivity, and specificity of detecting and differentiating dysphonic voices.
RESULTS: The accuracy, sensitivity, and specificity of OSELM in detecting normal and dysphonic voices were 90%, 98%, and 73%, respectively. The classifier differentiated between structural and non-structural vocal fold pathology with accuracy, sensitivity, and specificity of 84%, 89%, and 88%, respectively, while it differentiated between malignant and benign lesions with an accuracy, sensitivity, and specificity of 92%, 100%, and 58%, respectively. Compared to other classifiers, OSELM showed superior accuracy and sensitivity in detecting dysphonic voices, differentiating structural versus non-structural vocal fold pathology, and between malignant and benign voice pathology.
CONCLUSION: The OSELM algorithm exhibited the highest accuracy and sensitivity compared to other classifiers in detecting voice pathology, classifying between malignant and benign lesions, and differentiating between structural and non-structural vocal pathology. Hence, it is a promising artificial intelligence that supports an online application to be used as a screening tool to encourage people to seek medical consultation early for a definitive diagnosis of voice pathology.

Matched MeSH terms: Machine Learning*
Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors

Shiammala PN, Duraimutharasan NKB, Vaseeharan B, Alothaim AS, Al-Malki ES, Snekaa B, et al.

Methods, 2023 Nov;219:82-94.
PMID: 37778659 DOI: 10.1016/j.ymeth.2023.09.010

Artificial intelligence (AI), particularly deep learning as a subcategory of AI, provides opportunities to accelerate and improve the process of discovering and developing new drugs. The use of AI in drug discovery is still in its early stages, but it has the potential to revolutionize the way new drugs are discovered and developed. As AI technology continues to evolve, it is likely that AI will play an even greater role in the future of drug discovery. AI is used to identify new drug targets, design new molecules, and predict the efficacy and safety of potential drugs. The inclusion of AI in drug discovery can screen millions of compounds in a matter of hours, identifying potential drug candidates that would have taken years to find using traditional methods. AI is highly utilized in the pharmaceutical industry by optimizing processes, reducing waste, and ensuring quality control. This review covers much-needed topics, including the different types of machine-learning techniques, their applications in drug discovery, and the challenges and limitations of using machine learning in this field. The state-of-the-art of AI-assisted pharmaceutical discovery is described, covering applications in structure and ligand-based virtual screening, de novo drug creation, prediction of physicochemical and pharmacokinetic properties, drug repurposing, and related topics. Finally, many obstacles and limits of present approaches are outlined, with an eye on potential future avenues for AI-assisted drug discovery and design.

Matched MeSH terms: Machine Learning*
Fulltext Multi-population Black Hole Algorithm for the problem of data clustering

Salih SQ, Alsewari AA, Wahab HA, Mohammed MKA, Rashid TA, Das D, et al.

PLoS One, 2023;18(7):e0288044.
PMID: 37406006 DOI: 10.1371/journal.pone.0288044

The retrieval of important information from a dataset requires applying a special data mining technique known as data clustering (DC). DC classifies similar objects into a groups of similar characteristics. Clustering involves grouping the data around k-cluster centres that typically are selected randomly. Recently, the issues behind DC have called for a search for an alternative solution. Recently, a nature-based optimization algorithm named Black Hole Algorithm (BHA) was developed to address the several well-known optimization problems. The BHA is a metaheuristic (population-based) that mimics the event around the natural phenomena of black holes, whereby an individual star represents the potential solutions revolving around the solution space. The original BHA algorithm showed better performance compared to other algorithms when applied to a benchmark dataset, despite its poor exploration capability. Hence, this paper presents a multi-population version of BHA as a generalization of the BHA called MBHA wherein the performance of the algorithm is not dependent on the best-found solution but a set of generated best solutions. The method formulated was subjected to testing using a set of nine widespread and popular benchmark test functions. The ensuing experimental outcomes indicated the highly precise results generated by the method compared to BHA and comparable algorithms in the study, as well as excellent robustness. Furthermore, the proposed MBHA achieved a high rate of convergence on six real datasets (collected from the UCL machine learning lab), making it suitable for DC problems. Lastly, the evaluations conclusively indicated the appropriateness of the proposed algorithm to resolve DC issues.

Matched MeSH terms: Machine Learning*
Shared-learning experience during a clinical pharmacy practice experience

Zakaria SF, Awaisu A

Am J Pharm Educ, 2011 May 10;75(4):75.
PMID: 21769151

OBJECTIVE: To implement a shared learning approach through fourth-year students' mentorship of third-year students and to assess the perceptions of the mentored students on the value of their shared learning experience.
DESIGN: We introduced the shared learning experience in clinical pharmacy and pharmacotherapeutic practice experiences involving 87 third-year and 51 fourth-year students. Both student groups undertook the practice experiences together, with third-year students working in smaller groups mentored by fourth-year students.
ASSESSMENT: A majority of the students (> 75%) believed that they learned to work as a team during their practice experiences and that the shared learning approach provided an opportunity to practice their communication skills. Similarly, most respondents (> 70%) agreed that the new approach would help them become effective members of the healthcare team and would facilitate their professional relationships in future practice. Almost two-thirds of the students believed that the shared learning enhanced their ability to understand clinical problems. However, about 31% of the pharmacy students felt that they could have learned clinical problem-solving skills equally well working only with peers from their own student group.
CONCLUSIONS: The pharmacy students in the current study generally believed that the shared-learning approach enhanced their ability to understand clinical problems and improved their communication and teamwork skills. Both groups of students were positive that they had acquired some skills through the shared-learning approach.

Matched MeSH terms: Learning*
Exploratory study on student perception on the use of chat AI in process-driven problem-based learning

Hamid H, Zulkifli K, Naimat F, Che Yaacob NL, Ng KW

Curr Pharm Teach Learn, 2023 Dec;15(12):1017-1025.
PMID: 37923639 DOI: 10.1016/j.cptl.2023.10.001

INTRODUCTION: With the increasing prevalence of artificial intelligence (AI) technology, it is imperative to investigate its influence on education and the resulting impact on student learning outcomes. This includes exploring the potential application of AI in process-driven problem-based learning (PDPBL). This study aimed to investigate the perceptions of students towards the use of ChatGPT) build on GPT-3.5 in PDPBL in the Bachelor of Pharmacy program.
METHODS: Eighteen students with prior experience in traditional PDPBL processes participated in the study, divided into three groups to perform PDPBL sessions with various triggers from pharmaceutical chemistry, pharmaceutics, and clinical pharmacy fields, while utilizing chat AI provided by ChatGPT to assist with data searching and problem-solving. Questionnaires were used to collect data on the impact of ChatGPT on students' satisfaction, engagement, participation, and learning experience during the PBL sessions.
RESULTS: The survey revealed that ChatGPT improved group collaboration and engagement during PDPBL, while increasing motivation and encouraging more questions. Nevertheless, some students encountered difficulties understanding ChatGPT's information and questioned its reliability and credibility. Despite these challenges, most students saw ChatGPT's potential to eventually replace traditional information-seeking methods.
CONCLUSIONS: The study suggests that ChatGPT has the potential to enhance PDPBL in pharmacy education. However, further research is needed to examine the validity and reliability of the information provided by ChatGPT, and its impact on a larger sample size.

Matched MeSH terms: Problem-Based Learning*
Environmental enrichment improves cognitive function, learning, memory and anxiety-related behaviours in rodent models of dementia: Implications for future study

Mohd Sahini SN, Mohd Nor Hazalin NA, Srikumar BN, Jayasingh Chellammal HS, Surindar Singh GK

Neurobiol Learn Mem, 2024 Feb;208:107880.
PMID: 38103676 DOI: 10.1016/j.nlm.2023.107880

Environmental enrichment (EE) is a process of brain stimulation by modifying the surroundings, for example, by changing the sensory, social, or physical conditions. Rodents have been used in such experimental strategies through exposure to diverse physical, social, and exploration conditions. The present study conducted an extensive analysis of the existing literature surrounding the impact of EE on dementia rodent models. The review emphasised the two principal aspects that are very closely related to dementia: cognitive function (learning and memory) as well as psychological factors (anxiety-related behaviours such as phobias and unrealistic worries). Also highlighted were the mechanisms involved in the rodent models of dementia showing EE effects. Two search engines, PubMed and Science Direct, were used for data collection using the following keywords: environmental enrichment, dementia, rodent model, cognitive performance, and anxiety-related behaviour. Fifty-five articles were chosen depending on the criteria for inclusion and exclusion. The rodent models with dementia demonstrated improved learning and memory in the form of hampered inflammatory responses, enhanced neuronal plasticity, and sustained neuronal activity. EE housing also prevented memory impairment through the prevention of amyloid beta (Aβ) seeding formation, an early stage of Aβ plaque formation. The rodents subjected to EE were observed to present increased exploratory activity and exert less anxiety-related behaviour, compared to those in standard housing. However, some studies have proposed that EE intervention through exercise would be too mild to counteract the anxiety-related behaviour and risk assessment behaviour deficits in the Alzheimer's disease rodent model. Future studies should be conducted on old-aged rodents and the duration of EE exposure that would elicit the greatest benefits since the existing studies have been conducted on a range of ages and EE durations. In summary, EE had a considerable effect on dementia rodent models, with the most evident being improved cognitive function.

Matched MeSH terms: Maze Learning/physiology
Fulltext HDG-select: A novel GUI based application for gene selection and classification in high dimensional datasets

Hameed SS, Hassan R, Hassan WH, Muhammadsharif FF, Latiff LA

PLoS One, 2021;16(1):e0246039.
PMID: 33507983 DOI: 10.1371/journal.pone.0246039

The selection and classification of genes is essential for the identification of related genes to a specific disease. Developing a user-friendly application with combined statistical rigor and machine learning functionality to help the biomedical researchers and end users is of great importance. In this work, a novel stand-alone application, which is based on graphical user interface (GUI), is developed to perform the full functionality of gene selection and classification in high dimensional datasets. The so-called HDG-select application is validated on eleven high dimensional datasets of the format CSV and GEO soft. The proposed tool uses the efficient algorithm of combined filter-GBPSO-SVM and it was made freely available to users. It was found that the proposed HDG-select outperformed other tools reported in literature and presented a competitive performance, accessibility, and functionality.

Matched MeSH terms: Machine Learning*
Fulltext Analysis of banana plant health using machine learning techniques

Thiagarajan JD, Kulkarni SV, Jadhav SA, Waghe AA, Raja SP, Rajagopal S, et al.

Sci Rep, 2024 Jul 01;14(1):15041.
PMID: 38951552 DOI: 10.1038/s41598-024-63930-y

The Indian economy is greatly influenced by the Banana Industry, necessitating advancements in agricultural farming. Recent research emphasizes the imperative nature of addressing diseases that impact Banana Plants, with a particular focus on early detection to safeguard production. The urgency of early identification is underscored by the fact that diseases predominantly affect banana plant leaves. Automated systems that integrate machine learning and deep learning algorithms have proven to be effective in predicting diseases. This manuscript examines the prediction and detection of diseases in banana leaves, exploring various diseases, machine learning algorithms, and methodologies. The study makes a contribution by proposing two approaches for improved performance and suggesting future research directions. In summary, the objective is to advance understanding and stimulate progress in the prediction and detection of diseases in banana leaves. The need for enhanced disease identification processes is highlighted by the results of the survey. Existing models face a challenge due to their lack of rotation and scale invariance. While algorithms such as random forest and decision trees are less affected, initially convolutional neural networks (CNNs) is considered for disease prediction. Though the Convolutional Neural Network models demonstrated impressive accuracy in many research but it lacks in invariance to scale and rotation. Moreover, it is observed that due its inherent design it cannot be combined with feature extraction methods to identify the banana leaf diseases. Due to this reason two alternative models that combine ANN with scale-invariant Feature transform (SIFT) model or histogram of oriented gradients (HOG) combined with local binary patterns (LBP) model are suggested. The first model ANN with SIFT identify the disease by using the activation functions to process the features extracted by the SIFT by distinguishing the complex patterns. The second integrate the combined features of HOG and LBP to identify the disease thus by representing the local pattern and gradients in an image. This paves a way for the ANN to learn and identify the banana leaf disease. Moving forward, exploring datasets in video formats for disease detection in banana leaves through tailored machine learning algorithms presents a promising avenue for research.

Matched MeSH terms: Machine Learning*

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