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Ingatan palsu dalam kalangan kanak-kanak Pra Sekolah: satu bukti kewujudan ingatan palsu daripada eksperimen ingatan

Khairudin R, Charyna AR

Jurnal Psikologi Malaysia, 2010;olume 24:111-121.

This study aimed to investigate the occurrence of false memory in preschool children aged 3 to 6 years old. Three experiments were conducted using the traditional measurements of memory: free recall (experiment 1), cued recall (experiment 2) and recognition (experiment 3). A total of 24 children who were divided into three groups of 8, participated in the study. Result of experiment 1 showed a significant effect of categorical information on false memory. Preschool children were more likely to be influenced by false memory when the information was carried object category. Results if experiment 2 showed a significant difference in types of questions. More false memories were created for misleading questions than leading questions. Experiment 3 demonstrated that the preschool children were more likely to choose distracter pictures that target pictures. Within the distracter pictures, participants chose more false distracters than leading distracters. Implication of the findings suggests a strong evidence for the occurence of false memory in preschool children. The size of false memory effect was determined by the type of information presented to the children.

Matched MeSH terms: Memory
Long memory and asymmetric volatility behaviour of the Malaysian stock market: a statistical modelling approach

Abu Hassan Shaari Mohd Nor, Chin WC

Sains Malaysiana, 2006;35:67-73.

This paper analyzes the asymmetric long memory volatility dependency of the interday prices of Composite Index (CI) at Bursa Malaysia by using GARCH family models. The GARCH type models are used with the assumption that the innovations series follow either one of the following distributions: Gaussian, Student -t and skewed Student -t. The stock returns' long memory dependency is determined using the Hurst parameter. The long memory and asymmetric volatility are modelled by fractionally integrated GARCH models. It is found that the asymmetric and long memory GARCH models with skewed student-t distribution give better predictive ability on the volatility of the Kuala Lumpur Composite Index (KLCI).

Matched MeSH terms: Memory
Fractionally integrated time-varying volatility under structural break: evidence from Kuala Lumpur composite index

Chin WC, Zaidi Isa, Abu Hassan Shaari Mohd. Nor

Sains Malaysiana, 2008;37:233-237.

This article study the influences of structural break to the fractionally integrated time-varying volatility model in Malaysian stock markets from year 1996 to 2006. A fractionally integrated autoregressive conditional heteroscedastic (FIGARCH) model combines with sudden changes of volatility is develops to study the possibility of structural change in Asian financial crisis and currency crisis. Our empirical results evidence substantially reduction in long memory clustering volatility after the inclusion of sudden changes in the volatility. Finally, the estimation, diagnostic and model selection evaluations indicate that the fractionally integrated model with structural change is out-performed compared to the standard model.

Matched MeSH terms: Memory
Fulltext Incoming Work-In-Progress Prediction in Semiconductor Fabrication Foundry Using Long Short-Term Memory

Tin TC, Chiew KL, Phang SC, Sze SN, Tan PS

Comput Intell Neurosci, 2019;2019:8729367.
PMID: 30719036 DOI: 10.1155/2019/8729367

Preventive maintenance activities require a tool to be offline for long hour in order to perform the prescribed maintenance activities. Although preventive maintenance is crucial to ensure operational reliability and efficiency of the tool, long hour of preventive maintenance activities increases the cycle time of the semiconductor fabrication foundry (Fab). Therefore, this activity is usually performed when the incoming Work-in-Progress to the equipment is forecasted to be low. The current statistical forecasting approach has low accuracy because it lacks the ability to capture the time-dependent behavior of the Work-in-Progress. In this paper, we present a forecasting model that utilizes machine learning method to forecast the incoming Work-In-Progress. Specifically, our proposed model uses LSTM to forecast multistep ahead incoming Work-in-Progress prediction to an equipment group. The proposed model's prediction results were compared with the results of the current statistical forecasting method of the Fab. The experimental results demonstrated that the proposed model performed better than the statistical forecasting method in both hit rate and Pearson's correlation coefficient, r.

Matched MeSH terms: Memory, Short-Term/physiology*; Memory, Long-Term/physiology*
Fulltext Hybrid Multi-Level Detection and Mitigation of Clone Attacks in Mobile Wireless Sensor Network (MWSN)

Shaukat HR, Hashim F, Shaukat MA, Ali Alezabi K

Sensors (Basel), 2020 Apr 17;20(8).
PMID: 32316487 DOI: 10.3390/s20082283

Wireless sensor networks (WSNs) are often deployed in hostile environments, where an adversary can physically capture some of the sensor nodes. The adversary collects all the nodes' important credentials and subsequently replicate the nodes, which may expose the network to a number of other security attacks, and eventually compromise the entire network. This harmful attack where a single or more nodes illegitimately claims an identity as replicas is known as the node replication attack. The problem of node replication attack can be further aggravated due to the mobile nature in WSN. In this paper, we propose an extended version of multi-level replica detection technique built on Danger Theory (DT), which utilizes a hybrid approach (centralized and distributed) to shield the mobile wireless sensor networks (MWSNs) from clone attacks. The danger theory concept depends on a multi-level of detections; first stage (highlights the danger zone (DZ) by checking the abnormal behavior of mobile nodes), second stage (battery check and random number) and third stage (inform about replica to other networks). The DT method performance is highlighted through security parameters such as false negative, energy, detection time, communication overhead and delay in detection. The proposed approach also demonstrates that the hybrid DT method is capable and successful in detecting and mitigating any malicious activities initiated by the replica. Nowadays, crimes are vastly increasing and it is crucial to modify the systems accordingly. Indeed, it is understood that the communication needs to be secured by keen observation at each level of detection. The simulation results show that the proposed approach overcomes the weaknesses of the previous and existing centralized and distributed approaches and enhances the performance of MWSN in terms of communication and memory overhead.

Matched MeSH terms: Memory
Fulltext The Effect of Dataset Imbalance on the Performance of SCADA Intrusion Detection Systems

Balla A, Habaebi MH, Elsheikh EAA, Islam MR, Suliman FM

Sensors (Basel), 2023 Jan 09;23(2).
PMID: 36679553 DOI: 10.3390/s23020758

Integrating IoT devices in SCADA systems has provided efficient and improved data collection and transmission technologies. This enhancement comes with significant security challenges, exposing traditionally isolated systems to the public internet. Effective and highly reliable security devices, such as intrusion detection system (IDSs) and intrusion prevention systems (IPS), are critical. Countless studies used deep learning algorithms to design an efficient IDS; however, the fundamental issue of imbalanced datasets was not fully addressed. In our research, we examined the impact of data imbalance on developing an effective SCADA-based IDS. To investigate the impact of various data balancing techniques, we chose two unbalanced datasets, the Morris power dataset, and CICIDS2017 dataset, including random sampling, one-sided selection (OSS), near-miss, SMOTE, and ADASYN. For binary classification, convolutional neural networks were coupled with long short-term memory (CNN-LSTM). The system's effectiveness was determined by the confusion matrix, which includes evaluation metrics, such as accuracy, precision, detection rate, and F1-score. Four experiments on the two datasets demonstrate the impact of the data imbalance. This research aims to help security researchers in understanding imbalanced datasets and their impact on DL SCADA-IDS.

Matched MeSH terms: Memory, Long-Term
Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment

Latif SD

Environ Sci Pollut Res Int, 2021 Jun;28(23):30294-30302.
PMID: 33590396 DOI: 10.1007/s11356-021-12877-y

One of the most critical parameters in concrete design is compressive strength. As the compressive strength of concrete is correctly measured, time and cost can be decreased. Concrete strength is relatively resilient to impacts on the environment. The production of concrete compressive strength is greatly influenced by severe weather conditions and increases in humidity rates. In this research, a model has been developed to predict concrete compressive strength utilizing a detailed dataset obtained from previously published studies based on a deep learning method, namely, long short-term memory (LSTM), and a conventional machine learning (ML) algorithm, namely, support vector machine (SVM). The input variables of the model include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age of specimens. To demonstrate the efficiency of the proposed models, three statistical indices, namely, the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE), were used. Findings shows that LSTM outperformed SVM with R2=0.98, R2= 0.78, MAE=1.861, MAE=6.152, and RMSE=2.36, RMSE=7.93, respectively. The results of this study suggest that high-performance concrete (HPC) compressive strength can be reliably measured using the proposed LSTM model.

Matched MeSH terms: Memory, Short-Term
Fulltext Fake news stance detection using selective features and FakeNET

Aljrees T, Cheng X, Ahmed MM, Umer M, Majeed R, Alnowaiser K, et al.

PLoS One, 2023;18(7):e0287298.
PMID: 37523404 DOI: 10.1371/journal.pone.0287298

The proliferation of fake news has severe effects on society and individuals on multiple fronts. With fast-paced online content generation, has come the challenging problem of fake news content. Consequently, automated systems to make a timely judgment of fake news have become the need of the hour. The performance of such systems heavily relies on feature engineering and requires an appropriate feature set to increase performance and robustness. In this context, this study employs two methods for reducing the number of feature dimensions including Chi-square and principal component analysis (PCA). These methods are employed with a hybrid neural network architecture of convolutional neural network (CNN) and long short-term memory (LSTM) model called FakeNET. The use of PCA and Chi-square aims at utilizing appropriate feature vectors for better performance and lower computational complexity. A multi-class dataset is used comprising 'agree', 'disagree', 'discuss', and 'unrelated' classes obtained from the Fake News Challenges (FNC) website. Further contextual features for identifying bogus news are obtained through PCA and Chi-Square, which are given nonlinear characteristics. The purpose of this study is to locate the article's perspective concerning the headline. The proposed approach yields gains of 0.04 in accuracy and 0.20 in the F1 score, respectively. As per the experimental results, PCA achieves a higher accuracy of 0.978 than both Chi-square and state-of-the-art approaches.

Matched MeSH terms: Memory, Long-Term
Fulltext Tuning attention based long-short term memory neural networks for Parkinson's disease detection using modified metaheuristics

Cuk A, Bezdan T, Jovanovic L, Antonijevic M, Stankovic M, Simic V, et al.

Sci Rep, 2024 Feb 21;14(1):4309.
PMID: 38383690 DOI: 10.1038/s41598-024-54680-y

Parkinson's disease (PD) is a progressively debilitating neurodegenerative disorder that primarily affects the dopaminergic system in the basal ganglia, impacting millions of individuals globally. The clinical manifestations of the disease include resting tremors, muscle rigidity, bradykinesia, and postural instability. Diagnosis relies mainly on clinical evaluation, lacking reliable diagnostic tests and being inherently imprecise and subjective. Early detection of PD is crucial for initiating treatments that, while unable to cure the chronic condition, can enhance the life quality of patients and alleviate symptoms. This study explores the potential of utilizing long-short term memory neural networks (LSTM) with attention mechanisms to detect Parkinson's disease based on dual-task walking test data. Given that the performance of networks is significantly inductance by architecture and training parameter choices, a modified version of the recently introduced crayfish optimization algorithm (COA) is proposed, specifically tailored to the requirements of this investigation. The proposed optimizer is assessed on a publicly accessible real-world clinical gait in Parkinson's disease dataset, and the results demonstrate its promise, achieving an accuracy of 87.4187 % for the best-constructed models.

Matched MeSH terms: Memory, Short-Term
Fulltext Brain Activation during Addition and Subtraction Tasks In-Noise and In-Quiet

Abd Hamid AI, Yusoff AN, Mukari SZ, Mohamad M

Malays J Med Sci, 2011 Apr;18(2):3-15.
PMID: 22135581 MyJurnal

In spite of extensive research conducted to study how human brain works, little is known about a special function of the brain that stores and manipulates information-the working memory-and how noise influences this special ability. In this study, Functional magnetic resonance imaging (fMRI) was used to investigate brain responses to arithmetic problems solved in noisy and quiet backgrounds.

Matched MeSH terms: Memory, Short-Term
Fulltext The influence of colour on memory performance: a review

Dzulkifli MA, Mustafar MF

Malays J Med Sci, 2013 Mar;20(2):3-9.
PMID: 23983571 MyJurnal

Human cognition involves many mental processes that are highly interrelated, such as perception, attention, memory, and thinking. An important and core cognitive process is memory, which is commonly associated with the storing and remembering of environmental information. An interesting issue in memory research is on ways to enhance memory performance, and thus, remembering of information. Can colour result in improved memory abilities? The present paper highlights the relationship between colours, attention, and memory performance. The significance of colour in different settings is presented first, followed by a description on the nature of human memory. The role of attention and emotional arousal on memory performance is discussed next. The review of several studies on colours and memory are meant to explain some empirical works done in the area and related issues that arise from such studies.

Matched MeSH terms: Memory
Goat milk attenuates mimetic aging related memory impairment via suppressing brain oxidative stress, neurodegeneration and modulating neurotrophic factors in D-galactose-induced aging model

Safdar A, Zakaria R, Aziz CBA, Rashid U, Azman KF

Biogerontology, 2020 04;21(2):203-216.
PMID: 31792648 DOI: 10.1007/s10522-019-09854-x

One of the most significant hallmarks of aging is cognitive decline. D-galactose administration may impair memory and mimic the effects of natural aging. In this study, the efficiency of goat milk to protect against memory decline was tested. Fifty-two male Sprague-Dawley rats were randomly divided into four groups: (i) control group, (ii) goat milk treated group, (iii) D-galactose treated group, and (iv) goat milk plus D-galactose treated group. Subcutaneous injections of D-galactose at 120 mg/kg and oral administrations of goat milk at 1 g/kg were chosen for the study. Goat milk and D-galactose were administered concomitantly for 6 weeks, while the control group received saline. After 6 weeks, novel object recognition and T-maze tests were performed to evaluate memory of rats. Following behavioral tests, the animals were sacrificed, and right brain homogenates were analyzed for levels of lipid peroxidation, antioxidant enzymes and neurotrophic factors. The left brain hemisphere was used for histological study of prefrontal cortex and hippocampus. There was a significant memory impairment, an increase in oxidative stress and neurodegeneration and a reduction in antioxidant enzymes and neurotrophic factors levels in the brain of D-galactose treated rats compared to controls. Goat milk treatment attenuated memory impairment induced by D-galactose via suppressing oxidative stress and neuronal damage and increasing neurotrophic factors levels, thereby suggesting its potential role as a geroprotective food.

Matched MeSH terms: Memory*; Memory Disorders/chemically induced; Memory Disorders/metabolism; Memory Disorders/pathology; Memory Disorders/prevention & control*
Prevalence and factors of verbal learning and memory dysfunction in patients with epilepsy - A single centre study

Subramaniam SR, Khoo CS, Raymond AA, Che Din N, Syed Zakaria SZ, Tan HJ

J Clin Neurosci, 2020 Mar;73:31-36.
PMID: 32094071 DOI: 10.1016/j.jocn.2020.02.003

The objective of this study is to determine prevalence and factors leading to verbal learning and memory dysfunction among patients with epilepsy. A total of 211 subjects were recruited. Their verbal memory was assessed by Rey's Auditory Verbal Learning Test (RAVLT). This test was further subdivided into four major spheres for analysis, namely the verbal learning, interference list, immediate memory and delayed memory. All data collected were analyzed using Statistical Package for Social Sciences. Among the 211 patients, 55% (n = 116) had focal seizures and the remaining 45% (n = 95) had generalized seizures. Prevalence of verbal learning and memory impairment was high at 39.97% overall, and found most commonly in patients with focal impaired awareness seizures. Verbal learning and immediate memory dysfunction were significantly lower in focal impaired awareness group compared to others. Age more than 50 years, exposure to three or more antiepileptic drugs and use of carbamazepine more than 1000 mg a day were the predictors in poor verbal memory outcome. No statistical difference was observed in the mean RAVLT scores among the gender and hand dominance groups. Between patients with and without electroencephalogram changes as well as brain magnetic resonance imaging changes, the mean RAVLT scores showed no statistically significant difference. Verbal learning and memory impairment is prevalent among the epilepsy patients. The consequences of the memory impairment can be as debilitating as the seizure control. RAVLT is a reliable and practical test in the clinical setting.

Matched MeSH terms: Memory Disorders/chemically induced; Memory Disorders/epidemiology*; Memory Disorders/psychology; Memory, Short-Term/drug effects; Memory, Short-Term/physiology
Fulltext Nicotine-prevented learning and memory impairment in REM sleep-deprived rat is modulated by DREAM protein in the hippocampus

Abd Rashid N, Hapidin H, Abdullah H, Ismail Z, Long I

Brain Behav, 2017 06;7(6):e00704.
PMID: 28638710 DOI: 10.1002/brb3.704

INTRODUCTION: REM sleep deprivation is associated with impairment in learning and memory, and nicotine treatment has been shown to attenuate this effect. Recent studies have demonstrated the importance of DREAM protein in learning and memory processes. This study investigates the association of DREAM protein in REM sleep-deprived rats hippocampus upon nicotine treatment.
METHODS: Male Sprague Dawley rats were subjected to normal condition, REM sleep deprivation and control wide platform condition for 72 hr. During this procedure, saline or nicotine (1 mg/kg) was given subcutaneously twice a day. Then, Morris water maze (MWM) test was used to assess learning and memory performance of the rats. The rats were sacrificed and the brain was harvested for immunohistochemistry and Western blot analysis.
RESULTS: MWM test found that REM sleep deprivation significantly impaired learning and memory performance without defect in locomotor function associated with a significant increase in hippocampus DREAM protein expression in CA1, CA2, CA3, and DG regions and the mean relative level of DREAM protein compared to other experimental groups. Treatment with acute nicotine significantly prevented these effects and decreased expression of DREAM protein in all the hippocampus regions but only slightly reduce the mean relative level of DREAM protein.
CONCLUSION: This study suggests that changes in DREAM protein expression in CA1, CA2, CA3, and DG regions of rat's hippocampus and mean relative level of DREAM protein may involve in the mechanism of nicotine treatment-prevented REM sleep deprivation-induced learning and memory impairment in rats.

Matched MeSH terms: Memory/drug effects; Memory/physiology; Memory Disorders/metabolism; Memory Disorders/prevention & control
Aggregated effects of combining daily milk consumption and aerobic exercise on short-term memory and sustained attention among female students

Leong IT, Moghadam S, Hashim HA

Percept Mot Skills, 2015 Feb;120(1):57-66.
PMID: 25621523 DOI: 10.2466/22.06.PMS.120v11x3

Regular aerobic exercise and milk consumption have been found to have positive effects on certain cognitive functions such as short-term memory and sustained attention. However, aggregated effects of combining these modalities have not been explored. This study examined the combined effects of milk supplementation and aerobic exercise on the short-term memory and sustained attention of female students aged 16 yr. (N = 81). The intervention involved serving of 250 ml of regular milk during school days and/or a 1-hr. aerobic exercise period twice per week for 6 weeks. The Digit Span Test and Digit Vigilance Test were used to measure short-term memory and sustained attention, respectively. The combination group (milk and exercise) and exercise group performed significantly better than did the milk and control groups in terms of short-term memory. No significant interaction or group differences were found for sustained attention. The results suggest benefits of regular exercise for students' short-term memory.

Matched MeSH terms: Memory, Short-Term/physiology*
Human memory retention and recall processes. A review of EEG and fMRI studies

Amin H, Malik AS

Neurosciences (Riyadh), 2013 Oct;18(4):330-44.
PMID: 24141456

Human memory is an important concept in cognitive psychology and neuroscience. Our brain is actively engaged in functions of learning and memorization. Generally, human memory has been classified into 2 groups: short-term/working memory, and long-term memory. Using different memory paradigms and brain mapping techniques, psychologists and neuroscientists have identified 3 memory processes: encoding, retention, and recall. These processes have been studied using EEG and functional MRI (fMRI) in cognitive and neuroscience research. This study reviews previous research reported for human memory processes, particularly brain behavior in memory retention and recall processes with the use of EEG and fMRI. We discuss issues and challenges related to memory research with EEG and fMRI techniques.

Matched MeSH terms: Memory/physiology*
What proteins are involved in learning and memory?

Perumal R, Tan I

IUBMB Life, 2007 Jul;59(7):465-8.
PMID: 17654123
Matched MeSH terms: Memory/physiology*
Effect of gestational ethanol exposure on long-term memory formation in newborn chicks

Rao V, Chaudhuri JD

Alcohol, 2007 Sep;41(6):433-9.
PMID: 17624712

Fetal alcohol syndrome (FAS), a condition occurring in some children of mothers who have consumed alcohol during pregnancy, is characterized by craniofacial malformations, and physical and mental retardation. It is significant that even children with history of gestational ethanol exposure but relatively unaffected overall IQ performance, often exhibit learning difficulties and behavioral problems, suggestive of impaired memory formation. Hence, the specific aim of this study was to examine memory formation in chicks exposed to ethanol during early gestation toward the understanding of neurobehavioral disturbances in FAS. Chicks were exposed to alcohol on gestational days 1-3 by injection of ethanol into the airspace of freshly fertilized eggs. The effects of prenatal ethanol on physical growth and development, and memory formation were studied. The one-trial passive avoidance learning paradigm in 1-day-old chicks was used to study memory formation in these chicks. It was observed that chick embryos exposed to 10% ethanol on gestational days 1-3 had significant reduction in all body parameters when compared with appropriate controls. Further, ethanol-exposed chick embryos had significantly impaired (P

Matched MeSH terms: Memory/drug effects*
Working memory and literacy as predictors of performance on algebraic word problems

Lee K, Ng SF, Ng EL, Lim ZY

J Exp Child Psychol, 2004 Oct;89(2):140-58.
PMID: 15388303 DOI: 10.1016/j.jecp.2004.07.001

Previous studies on individual differences in mathematical abilities have shown that working memory contributes to early arithmetic performance. In this study, we extended the investigation to algebraic word problem solving. A total of 151 10-year-olds were administered algebraic word problems and measures of working memory, intelligence quotient (IQ), and reading ability. Regression results were consistent with findings from the arithmetic literature showing that a literacy composite measure provided greater contribution than did executive function capacity. However, a series of path analyses showed that the overall contribution of executive function was comparable to that of literacy; the effect of executive function was mediated by that of literacy. Both the phonological loop and the visual spatial sketchpad failed to contribute directly; they contributed only indirectly by way of literacy and performance IQ, respectively.

Matched MeSH terms: Memory, Short-Term*
Activation of sphingosine 1-phosphate receptor-1 by SEW2871 improves cognitive function in Alzheimer's disease model rats

Asle-Rousta M, Oryan S, Ahmadiani A, Rahnema M

EXCLI J, 2013;12:449-61.
PMID: 26417237

Sphingosine-1 phosphate (S1P) is involved in a variety of cellular processes via activation of S1P receptors (S1PRs; S1PR1 to S1PR5) that are highly expressed in the brain. It has been shown that the level of S1P is reduced in the brain of Alzheimer's disease (AD) patients. However, there is no study designed to evaluate the expression of S1PRs in AD brains. The objectives of the present work are (1) to examine the expression of S1PR1-3 in the hippocampus of beta amyloid (Aβ) 1-42 injected rats and (2) to clarify the effects of chronic S1PR1 activation on S1PR1-3 levels, spatial memory deficit and hippocampal damage in AD rats. SEW2871, the S1PR1 selective agonist, repeatedly was injected intraperitoneally during a period of two weeks. Upon Western Blot data bilateral intrahippocampal injection of Aβ1-42 decreased the expression of S1PR1 while increased S1PR2 level and did not affect that of S1PR3. We found that chronic administration of SEW2871 inhibited the reduction of S1PR1 expression and ameliorated spatial memory impairment in the Morris water maze task in rats. In addition, SEW2871 attenuated the Aβ1-42-induced hippocampal neuronal loss according to Nissl staining findings. Data in the current study highlights the importance of S1PR1 signaling pathway deregulation in AD development and suggests that activation of S1PR1 may represent a potential approach for developing new therapeutics to manage memory deficit and apoptosis associated with neurodegenerative disorders such as AD.

Matched MeSH terms: Memory Disorders; Spatial Memory

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