Association Rule Learning

Human errors during software development lead to many defects, which emphasizes the importance of early detection and minimization. However, existing approaches often fall short in delivering accurate, scalable, and generalizable predictions due to challenges such as class imbalance, feature extraction limitations, and computational inefficiencies. This study proposes a hybrid method using a Convolutional Neural Network (CNNs) + Long Short-Term Memory (LSTM) for feature extraction, addressing class imbalance with Adaptive Synthetic Sampling (ADASYN) and subsequent training using Extreme Gradient Boosting (XGboost), to predict software defects. The proposed approach was evaluated on five publicly available datasets (CM1, MC1, KC1, PC1, and PC4) and compared with state-of-the-art (SOTA) models. Experimental results demonstrated that the hybrid model significantly outperforms traditional XGBoost-based models in terms of recall, F1-score, and area under the receiver operating characteristic curve (AUC), addressing the shortcomings of existing methods. Results demonstrate the effectiveness of the proposed method, with notable performance metrics achieved across all datasets. For example, on the MC1 dataset, the model attained an accuracy of 0.9980, a precision of 0.9971, a recall of 0.9988, an F1-score of 0.9980, and an AUC-ROC of 0.9999. On the KC1 dataset, it achieved an accuracy of 0.9344, a precision of 0.9265, a recall of 0.9375, an F1-score of 0.9320, and an AUC-ROC of 0.9839. The model achieves better performance than traditional machine learning methods and separate deep learning models, especially in the areas of recall and AUC-ROC. This research presents a robust solution through hybrid approaches that address class imbalance and maintain high predictive accuracy for software development process tasks, offering insights into the trade-offs between machine learning and deep learning methods.

Previous research has shown that intertrial repetition of target and distractors task-relevant properties speeds visual search performance, an effect known as priming of pop-out (PoP). Recent accounts suggest that such priming results, at least in part, from a mechanism that speeds post-selectional, responserelated processes, the marker of which is an interaction between repetition of the target and distractor features and repetition of the response from the previous trial. However, this response-based component of inter-trial priming has been elusive, and it remains unclear what its boundary conditions might be. In addition, what information is represented in the episodic memory traces that underlie the responsebased component has not yet been characterized. Here, we show that the response-based component of feature priming reflects an episodic memory retrieval mechanism that is not mandatory or automatic but may be described as a heuristic that subjects sometimes use, in particular when the overall difficulty of the search task is high. In addition, we show that the conjunction of the target and distractor features forms the context that is reactivated during episodic retrieval. Finally, we show that target-distractor discriminability is an important modulator of the selection-based component. The findings are discussed within the framework of the dual-stage model of inter-trial priming .

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Data Mining is the activity of analyzing data from different perspectives and incorporating it into useful information. From the last decades, number of students is interested and focuses their carrier in engineering meanwhile many private as well as government colleges came into existence. Now the students is having various viewpoint and thoughts regarding the colleges related to different perspectives. In this paper, the technique of data mining is used to find out the willingness of the students and to analyze the activities related to their performance. This will help the college management to get fruitful admissions in the successive years.

Ensemble learning is a leading approach in software defect prediction (SDP), offering improved predictive performance on imbalanced and high-dimensional datasets. Despite growing research interest, persistent gaps remain in model interpretability, generalizability, and reproducibility, limiting its practical adoption. This paper presents a comprehensive analysis of 56 peer-reviewed studies published between 2020 and 2025, spanning both journal and conference venues. Findings show that ensemble methods, especially when combined with sampling, feature selection, or optimisation, consistently outperform single classifiers on important metrics such as F1-score, area under the curve, and Matthew correlation coefficient. Nonetheless, few studies incorporate explainability frameworks, effort-aware evaluation, or cross-project validation. Additionally, most models are static, rely on within-project testing, and depend on legacy datasets such as PROMISE and NASA, which limit external validity. Building on this synthesis, the review highlights future research priorities, including interpretable ensemble architectures, adaptive modelling, dynamic imbalance handling, semantic feature integration, and real-time prediction. Standardised benchmarks, transparent, scalable designs are recommended to bridge the gap between experimental performance and deployment-ready SDP solutions.

Research on context-mediated facilitation of recognition memory distinguishes between the effects of reinstating the exact same context previously associated with a target and a context that is familiar but not directly associated with the target. As both effects are difficult to produce reliably in recognition experiments, attention has turned to measures that may explain inconsistencies, such as the extent to which instructions encourage association between targets and contexts. The aim of the current study was to examine the distinctive and interactive effects of three factors that may lead to variability in context effects (CEs), namely type of instructions given at learning, delay between learning and test, and exposure time for targets and contexts at learning. Using a comprehensive paradigm developed by Vakil and colleagues, with photographs of faces serving as target and context stimuli, both exposure time and delay were shown to be associated with the occurrence of CEs and appeared to interact with one another in determining the nature of these effects. Unlike several previous studies, false alarms did not increase when foils were presented with familiar contexts. Also unexpectedly, the instruction manipulation did not appear to strengthen target-context binding. It may instead have increased attention to contexts at the expense of targets, as suggested by the finding that direct memory for context improved under associative instruction conditions. Overall, the study demonstrates the importance of understanding and controlling various factors that may potentially influence the emergence of both reinstatement and familiaritybased CEs, among them exposure time and learning-to-test delay.

The information age is now passing through an evolutionary phase. As the capacity of networks is increasing to handle and process huge volume of information traffic, the management of such information systems is becoming a trivial issue. The Internet being the largest source of information provision is heterogeneous in nature with constantly evolving environment. New information services are added whereas some are modified or removed. User’s demands and interests also change constantly. In such a dynamic environment it is very difficult to maintain a coherent picture of the information services. A novel technique proposed recently employs autonomous mobile agents in faded information field architecture to facilitate information provision and servicing in this new era of information age. The architecture is decentralized in nature whereby both the service provider and information seeker communicate through mobile agents. However, the degree of fairness of information distribution and...

Neuropsychologists are developing more challenging and specific tests to detect early and subtle changes in cognition related to preclinical Alzheimer's disease (AD). The 16-item Face-Name Associative Memory Exam (FNAME-16) is a challenging paired associative memory test able to detect subtle memory changes associated with biomarker evidence of preclinical AD. However, as individuals progress along the AD trajectory, measures that are sensitive at the preclinical stage may become too challenging by the stage of Mild Cognitive Impairment (MCI). Our goal was to develop a modified version of the face-name and face-occupation paired associative memory task (FNAME-12) with fewer stimuli and additional learning trials suitable for use in MCI. We administered the FNAME-12A, an alternate version FNAME 12B, the original FNAME-16, and a series of other neuropsychological measures to 65 clinically normal (CN) older adults (aged 65 to 85) and a subsample characterized by MCI (n=18). The FNAME-12 exhibited psychometric equivalence with the FNAME-16 (r=0.77, p<.001) and was correlated with other measures of episodic and semantic memory. The alternate form, FNAME-12B, was highly correlated with FNAME-12A (r=0.76, p<.001). Mean performance on the FNAME 12A, stratified by education, was generated. The task was able to be completed by our MCI group yet remained challenging in the CN group, providing evidence of its utility along the AD trajectory.

The parallelization of mining algorithms under MapReduce (MR) became a reality in the last years, but algorithms for training single decision trees, like ID3 [1]or C4.5 [2], remain unexplored. Decision trees continue to play an important role in data mining, mainly in the cases where the model itself is used to understand or just validate existing domain knowledge. In this paper, we discuss the different issues to deal with when trying to parallelize ID3 under MR, and propose a new algorithm, MRID4, for training a single decision tree, based on ID3 but that is able to deal with numerical attributes, as in C4.5. Experimental results show that our algorithm can scale to very high values.

Despite the efforts made on last decades to center the process of knowledge discovery on the user, the balance between the discovery of unknown and interesting patterns is far from being reached. The discovery of association rules is a paradigmatic case, where this balance is quite difficult to establish. In this paper, we propose a new framework for pattern mining -the Onto4AR. This framework is centered on the use of ontologies, for the representation and introduction of domain knowledge into the mining process. By defining constraints based on an ontology, the framework provides a mining environment independent of the problem domain. With this simplification on the definition and use of constraints, the framework contributes to reduce the gap between discovered rules and user expectations.

Algorithms for the inference of association with sequential information have been proposed and used but are ineffective, in some cases, because too many candidate rules are extracted. Filtering the relevant ones is usually difficult and inefficient. In this work, we present an algorithm for the inference of temporal association rules that uses context-free grammars to restrict the search process, in order to filter, in an efficient and effective way, the associations discovered by the algorithm. Moreover, we present experimental results that empirically evaluate its performance using synthetic data.

The process of finding interesting patterns and knowledge from huge amount of data is known as Data Mining. Association rule mining is One of the most important techniques in this field is association rule mining. Association rule mining is a process which is meant to discover frequent patterns, correlations, associations, or causal structures from datasets. Feature selection is the process of selecting a subset of relevant features from large amount of data. Feature selection involves identifying a subset of the most useful features that reduces the size of original dataset. A feature selection algorithm may be evaluated from both the efficiency and effectiveness points of view. Efficiency is based on the time required to find a subset of features and the effectiveness is related to the quality of the subset of features. Based on these criteria, a fast clustering-based feature selection algorithm, FAST, is used[18]. The FAST algorithm works in two steps. In the first step, features are divided into clusters. In the second step, the most representative feature that is strongly related to target classes is selected from each cluster to form a subset of features. Features in different clusters are relatively independent; the clustering-based strategy of FAST has a high probability of producing a subset of useful and independent features. To ensure the efficiency of FAST, we adopt the efficient minimum-spanning tree clustering method. The efficiency and effectiveness of the FAST algorithm are evaluated through an empirical study.

The frontal association cortex (FrA) is implicated in higher brain function . Aberrant FrA activity is likely to be involved in dementia pathology . However, the functional circuits both within the FrA and with other regions are unclear. A recent study showed that inactivation of the FrA impairs memory consolidation of an auditory fear conditioning in young mice . In addition, dendritic spine remodeling of FrA neurons is sensitive to paired sensory stimuli that produce associative memory . These findings suggest that the FrA is engaged in neural processes critical to associative learning. Here we characterize stimulus integration in the mouse FrA during associative learning. We experimentally separated contextual fear conditioning into context exposure and shock, and found that memory formation requires protein synthesis associated with both context exposure and shock in the FrA. Both context exposure and shock trigger Arc, an activity-dependent immediate-early gene, expression in the FrA, and a subset of FrA neurons was dually activated by both stimuli. In addition, we found that the FrA receives projections from the perirhinal (PRh) and insular (IC) cortices and basolateral amygdala (BLA), which are implicated in context and shock encoding . PRh and IC neurons projecting to the FrA were activated by context exposure and shock, respectively. Arc expression in the FrA associated with context exposure and shock depended on PRh activity and both IC and BLA activities, respectively. These findings indicate that the FrA is engaged in stimulus integration and contributes to memory formation in associative learning. The Frontal Association Cortex Is Required for Memory Formation in Contextual Fear Conditioning As a model for associative learning, we used a contextual fearconditioning task, which establishes an association between context and shock. To test whether the frontal association cortex (FrA) is involved in memory formation in contextual fear conditioning, we infused (2R)-amino-5-phosphonovaleric acid (APV), an N-methyl-D-aspartate (NMDA) receptor antagonist, anisomycin, a protein synthesis inhibitor, or vehicle into the FrA. APV and anisomycin were infused 30 min prior to, or immediately after, contextual fear conditioning, respectively (experiment 1; Figures and). Contextual fear memory

The frontal association cortex (FrA) is implicated in higher brain function . Aberrant FrA activity is likely to be involved in dementia pathology . However, the functional circuits both within the FrA and with other regions are unclear. A recent study showed that inactivation of the FrA impairs memory consolidation of an auditory fear conditioning in young mice . In addition, dendritic spine remodeling of FrA neurons is sensitive to paired sensory stimuli that produce associative memory . These findings suggest that the FrA is engaged in neural processes critical to associative learning. Here we characterize stimulus integration in the mouse FrA during associative learning. We experimentally separated contextual fear conditioning into context exposure and shock, and found that memory formation requires protein synthesis associated with both context exposure and shock in the FrA. Both context exposure and shock trigger Arc, an activity-dependent immediate-early gene, expression in the FrA, and a subset of FrA neurons was dually activated by both stimuli. In addition, we found that the FrA receives projections from the perirhinal (PRh) and insular (IC) cortices and basolateral amygdala (BLA), which are implicated in context and shock encoding . PRh and IC neurons projecting to the FrA were activated by context exposure and shock, respectively. Arc expression in the FrA associated with context exposure and shock depended on PRh activity and both IC and BLA activities, respectively. These findings indicate that the FrA is engaged in stimulus integration and contributes to memory formation in associative learning. The Frontal Association Cortex Is Required for Memory Formation in Contextual Fear Conditioning As a model for associative learning, we used a contextual fearconditioning task, which establishes an association between context and shock. To test whether the frontal association cortex (FrA) is involved in memory formation in contextual fear conditioning, we infused (2R)-amino-5-phosphonovaleric acid (APV), an N-methyl-D-aspartate (NMDA) receptor antagonist, anisomycin, a protein synthesis inhibitor, or vehicle into the FrA. APV and anisomycin were infused 30 min prior to, or immediately after, contextual fear conditioning, respectively (experiment 1; Figures and). Contextual fear memory

Research on voluntary action has focused on the question of how we represent our behavior on a motor and cognitive level. However, the question of how we represent voluntary not acting has been completely neglected. The aim of the present study was to investigate the cognitive and motor representation of intentionally not acting. By using an action-effect binding approach, we demonstrate similarities of action and nonaction. In particular, our results reveal that voluntary nonactions can be bound to an effect tone. This finding suggests that effect binding is not restricted to an association between a motor representation and a successive effect (action-effect binding) but can also occur for an intended nonaction and its effect (nonaction-effect binding). Moreover, we demonstrate that nonactions have to be initiated voluntarily in order to elicit nonaction-effect binding.

Objective: Drawing on two different populations, Israeli police and Hungarian civilians, the present study assessed the ability of individuals with posttraumatic stress disorder (PTSD) to generalize previous learning to novel situations. Past neuroimaging studies have demonstrated diminished medial temporal lobe (MTL) activation and/or reduced hippocampal volume in individuals with PTSD. Our earlier computational models of cortico-hippocampal function and subsequent experimental tests of these models in MTL-impaired clinical populations argue that even mild hippocampal dysfunction may result in subtle impairments in generalization. Therefore, we predicted that individuals with PTSD would show impaired generalization. Method: We compared the performance of five groups from two countries, including 19 Israeli police with PTSD and 22 traumaexposed police without PTSD, and 22 Hungarian civilians with PTSD, 25 trauma-exposed civilians without PTSD, and 25 individuals without PTSD unexposed to the same trauma. Participants were tested on a two-phase learning paradigm, the Acquired Equivalence Task, which measures the ability to generalize past learning to novel situations. Results: We found that both PTSD and non-PTSD participants were capable of learning the initial stimulus-outcome associations, F(4, 108) ϭ 1.79, p ϭ .14. However, as predicted, only individuals with PTSD showed a selective deficit in generalization of this learning to novel situations (F(4, 108) ϭ 8.35, p Ͻ .001, Partial 2 ϭ 0.26). Conclusions: Individuals with PTSD show a selective impairment in generalization of past learning

Nowadays we find more and more applications for data mining techniques in e-learning and web-based adaptive educational systems. The useful information discovered can be used directly by the teacher or author of the course in order to improve instructional/learning performance. This can, however, imply a lot of work for the teacher who can greatly benefit from the help of educational recommender systems for doing this task. In this paper we propose a system oriented to find, share and suggest the most appropriate modifications to improve the effectiveness of the course. We describe an iterative methodology to develop and carry out the maintenance of web-based courses to which we have added a specific data mining step. We apply association rule mining to discover interesting information through students' usage data in the form of IF-THEN recommendation rules. We have also used a collaborative recommender system to share and score the recommendation rules obtained by teachers with similar profiles along with other experts in education. Finally, we have carried out experiments with several real groups of students using a web-based adaptive course. The results obtained demonstrate that the proposed architecture constitutes a good starting point to future investigations in order to generalize the results over many course contents.

Background Protein-protein interactions (PPI) can be classified according to their characteristics into, for example obligate or transient interactions. The identification and characterization of these PPI types may help in the functional annotation of new protein complexes and in the prediction of protein interaction partners by knowledge driven approaches. Results This work addresses pattern discovery of the interaction sites for four different interaction types to characterize and uses them for the prediction of PPI types employing Association Rule Based Classification (ARBC) which includes association rule generation and posterior classification. We incorporated domain information from protein complexes in SCOP proteins and identified 354 domain-interaction sites. 14 interface properties were calculated from amino acid and secondary structure composition and then used to generate a set of association rules characterizing these domain-interaction sites employing the APRIORI algor...

The objectives of the present study were to evaluate the impact of a single bout of high-intensity aerobic exercise on 1) long-term potentiation (LTP)-like neuroplasticity via response to paired associative stimulation (PAS) and 2) the temporal and spatial components of sequence-specific implicit motor learning. Additionally, relationships between exercise-induced increases in systemic brain-derived neurotrophic factor (BDNF) and response to PAS and motor learning were evaluated. Sixteen young healthy participants completed six experimental sessions, including the following: 1) rest followed by PAS; 2) aerobic exercise followed by PAS; 3) rest followed by practice of a continuous tracking (CT) task and 4) a no-exercise 24-h retention test; and 5) aerobic exercise followed by CT task practice and 6) a no-exercise 24-h retention test. The CT task included an embedded repeated sequence allowing for evaluation of sequence-specific implicit learning. Slope of motor-evoked potential recru...

Data mining can abstract important facts such as frequent item set from large data setbut sometimes it is difficult to achieve all frequent item set if these datasets are split into many clusters when there is a large dataset. In this paper, a detailed comparison has been made for the frequent pattern mining algorithms such as Apriori, ECLAT and FP-Growth based on without partitioning and with horizontal partitioning of dataset by applying actual minimum support as well as with dynamic minimum support. Experimental results shows that after partitioning also we can maintain similar accuracy and have obtained 100% frequent item set in contrast with frequent item set achieved without partitioning. This novel concept is useful to search frequent item set from the large dataset when we applied the concept of partitioning. It also shows that till which extent minimum support should be decreased with respect to number of cluster to achieve maximum accuracy or 100% frequent item set.

Association rule mining is a fundamental and vital functionality of data mining. M ost of the existing real time transactional databases are multidimensional in nature. In this paper, a novel algorithm is proposed for mining hybrid-dimensional association rules which are very useful in business decision making. The proposed algorithm uses multi index structures to store necessary details like item combination, support measure and transaction IDs, which stores all frequent 1-itemsets after scanning the entire database first time. Frequent k-itemsets are generated with previous level data, without scanning the database further. Compared to traditional algorithms, this algorithm efficiently finds association rules in multidimensional datasets, by scanning the database only once, thus enhancing the process of data mining.

Association rule mining is a fundamental and vital functionality of data mining. M ost of the existing real time transactional databases are multidimensional in nature. In this paper, a novel algorithm is proposed for mining hybrid-dimensional association rules which are very useful in business decision making. The proposed algorithm uses multi index structures to store necessary details like item combination, support measure and transaction IDs, which stores all frequent 1-itemsets after scanning the entire database first time. Frequent k-itemsets are generated with previous level data, without scanning the database further. Compared to traditional algorithms, this algorithm efficiently finds association rules in multidimensional datasets, by scanning the database only once, thus enhancing the process of data mining.

The problem of privacy-preserving data mining has become more important in recent years because of the increasing ability to store personal data about users, and the increasing sophistication of data mining algorithm to leverage this information. This paper is aimed at developing a protocol that will address the issue of privacy in data mining tasks in two party scenarios. We have proposed a framework that uses the homomorphic encryption to add security so that any data mining technique does not lose its valuable data. With the aid of this approach, confidentiality at both parties end is achieved. This model gives valid data mining results for analysis purpose but the actual or true data is not revealed. We discussed implementation evaluation based on metrics proposed by [1], for the framework and algorithm proposed. Tools used include PHP programming language for simulation, MYSQL for database and Visual Paradigm for the modeling analysis. Secondary sources such as academic literat...

Recent work has demonstrated substantial wiring and functional stereotypy in the fly olfactory system. In this issue of Neuron, Murthy et al. demonstrate that in the mushroom body, a site of olfactory associative learning, this initial peripheral stereotypy gives way to functionally nonstereotyped circuits.

The need for effective and efficient Denial of Service (DoS) Detection System cannot be overemphasized. This position is as a result of a serious threat to the availability of internet services that limit and block legitimate users access by exhausting victim server's resources or saturating stub networks access links to the internet services instead of subverting services. Hence the need for a supervised data learning techniques known as association rule mining which has the advantage of generating explainable rules was used to build a classifier for detecting some denial of service attacks, carry out a case study on International Knowledge Discovery and data Mining (KDD '99) tools, intrusion detection dataset for benchmarking the design of the intrusion detection systems. The average classification rate for unpruned rules is 63.16% while that of the pruned rules is 96.6%. The result revealed that pruned rule sets have better classification performance than the unpruned rule set.

Although studies examining orbitofrontal cortex (OFC) often treat it as though it were functionally homogeneous, recent evidence has questioned this assumption. Not only are the various subregions of OFC (lateral, ventral, and medial) hetereogeneous, but there is further evidence of heterogeneity within those subregions. For example, several studies in both humans and monkeys have revealed a functional subdivision along the anterior-posterior gradient of the medial OFC (mOFC). Given our previous findings suggesting that, in rats, the mOFC is responsible for inferring the likelihood of unobservable action outcomes , and given the anterior nature of the placements of our prior manipulations, we decided to assess whether the rat mOFC also differs in connection and function along its anteroposterior axis. We first used retrograde tracing to compare the density of efferents from mOFC to several structures known to contribute to goal-directed action: the mediodorsal thalamus, basolateral amygdala, posterior dorsomedial striatum, nucleus accumbens core and ventral tegmental area. We then compared the functional effects of anterior versus posterior mOFC excitotoxic lesions on tests of Pavlovian-instrumental transfer, instrumental outcome devaluation and outcome-specific reinstatement. We found evidence that the anterior mOFC had greater connectivity with the accumbens core and greater functional involvement in goaldirected action than the posterior mOFC. Consistent with previous findings across species, therefore, these results suggest that the anterior and posterior mOFC of the rat are indeed functionally distinct, and that it is the anterior mOFC that is particularly critical for inferring unobservable action outcomes.

Coffee is one of the plantation products that have an important position in economic activity in Indonesia. Coffee also has an important role as an export product in Indonesia's foreign exchange making . The Global Agricultural Information Network released the 2019 Indonesian Coffee Consumption Annual Data showing that Coffee Domestic Consumption for the 2019/2020 period of 294 thousand tons . This shows an increase in coffee consumption of around 13.9% compared to the 2018/2019 period which was only 258 thousand tons. According to the International Coffee Organization (ICO), the amount of coffee consumption is more than the amount of coffee produced in the year period of 2017 to 2020 which shows that there are a lot of coffee enthusiasts in Indonesia. The increase in coffee consumption can be seen in the proliferation of cafe or coffee shop businesses with a modern concept . The result of Toffin's research with Mix Magazine showed that in August 2019, the number of coffee shops in Indonesia was more than 2,950 outlets. This means that there is a threefold increase compared to 2016 when there were only 1,000 outlets . The increasing number of outlets can create intense competition among similar companies . Based on this, every company must find a strategy for running its business so as not to lose market share. One of the strategies that can be used to elevate the coffee business is through the implementation of Customer Relationship Management (CRM) . CRM techniques can be grouped into three categories, namely strategic, operational, and analytical . Analytical CRM is the process of storing, extracting, distributing, using, and reporting customer data into information that can be followed up well by the company .

The importance of recommendation systems is increasing day by day due to the massive number of data and information-overloaded arising from the internet. This data can be collected in predictive datasets; these datasets can be processed and analysed via data mining methods. In this paper, an efficient hybrid movie recommender system has been designed using the association rules mining technique and K-nearest neighbours (KNN) algorithm as a classification method. The K-nearest neighbours (KNN) algorithm subsystem was used to create the first candidate list through a practical MovieLens dataset, which was retrieved from the source of the NetFlix network. Beside, the Apriori algorithm subsystem is used to analyse the same dataset and create the second list. Finally, the proposed system creates a final recommended list by matching the two lists. The results of the proposed system provide better performance than the existing systems in terms of the important degree. The important degree ...

This paper aims to explain the web-enabled tools for educational data mining. The proposed web-based tool developed using Asp.Net framework and php can be helpful for universities or institutions providing the students with elective courses as well improving academic activities based on feedback collected from students. In Asp.Net tool, association rule mining using Apriori algorithm is used whereas in php based Feedback Analytical Tool, feedback related to faculty and institutional infrastructure is collected from students and based on that Feedback it shows performance of faculty and institution. Using that data, it helps management to improve in-house training skills and gains knowledge about educational trends which is to be followed by faculty to improve the effectiveness of the course and teaching skills.

The study of neurobiological mechanisms underlying anxiety disorders has been shaped by learning models that frame anxiety as maladaptive learning. Pavlovian conditioning and extinction are particularly influential in defining learning stages that can account for symptoms of anxiety disorders. Recently, dynamic and task related communication between the basolateral complex of the amygdala (BLA) and the medial prefrontal cortex (mPFC) has emerged as a crucial aspect of successful evaluation of threat and safety. Ongoing patterns of neural signaling within the mPFCBLA circuit during encoding, expression and extinction of adaptive learning are reviewed. The mechanisms whereby deficient mPFC-BLA interactions can lead to generalized fear are discussed in learned and innate anxiety. Findings with crossspecies validity are emphasized.

Frequent/Periodic item set mining is a extensively used data mining method for market based analysis,privacy preserving and it is also a heart favourite theme for the resarchers. A substantial work has been devoted to this research and tremendous progression made in this field so far. Frequent/Periodic itemset mining is used for search and to find back the relationship in a given data set. This paper introduces a new way which is more efficient in time and space frequent itemset mining. Our method scans the database only one time whereas the previous algorithms scans the database many times which utilizes more time and memory related to new one. In this way,the new algorithm will reduced the complexity (time & memory) of frequent pattern mining. We present efficient techniques to implement the new approach.

Frequent/Periodic item set mining is a extensively used data mining method for market based analysis,privacy preserving and it is also a heart favourite theme for the resarchers. A substantial work has been devoted to this research and tremendous progression made in this field so far. Frequent/Periodic itemset mining is used for search and to find back the relationship in a given data set. This paper introduces a new way which is more efficient in time and space frequent itemset mining. Our method scans the database only one time whereas the previous algorithms scans the database many times which utilizes more time and memory related to new one. In this way,the new algorithm will reduced the complexity (time & memory) of frequent pattern mining. We present efficient techniques to implement the new approach.

EasyMiner is a web-based visual interface for association rule learning. This paper presents a preview of the next release, which uses the R environment as the data processing backend. EasyMiner/R uses the arules package to learn rules. It uses the Classifications Based on Associations (CBA) algorithm as a classifier and to perform rule pruning. Experimental results show that EasyMiner with the R-based backend is able to handle larger datasets than the previous version.

This paper presents our recent work for participation in the First International Chinese Word Segmentation Bakeoff (ICWSB-1). It is based on a generalpurpose ngram model for word segmentation and a case-based learning approach to disambiguation. This system excels in identifying in-vocabulary (IV) words, achieving a recall of around 96-98%. Here we present our strategies for language model training and disambiguation rule learning, analyze the system's performance, and discuss areas for further improvement, e.g., out-of-vocabulary (OOV) word discovery.

Many universities have implemented innovative information systems and services to help their students and to support academic management processes. This paper proposes a conceptual framework to support Student Relationship Management (SRM) for Thai universities in the new electronic era. This conceptual framework assists students to choose appropriate course and subjects for their studies. The objectives of this article aim to broaden an understanding of SRM and related issues focus on recruitment, enrolment and course counseling. An Intelligent Recommendation System framework (IRS) has been designed using data mining and artificial intelligent techniques such as clustering, association rule and fuzzy logic. These techniques will be used to assess the performance of the students and to provide appropriate recommendations for their choice of courses and subjects.

Behavior is treated as basic physics. Dimensions are identified and their transformations from physical specification to axes in behavioral space are suggested. Responses are treated as action patterns arrayed along a continuum of activation energy. Behavior is seen as movement along a trajectory through this behavior space. Incentives or reinforcers are attractors in behavior space, at the centers of basins of lowered potential. Trajectories impinging on such basins may be captured; repeated capture will warp the trajectory toward a geodesic, a process called conditioning. Conditioning is enhanced by contiguity, the proximity between the measured behavior and the incentive at the end of the trajectory, and by contingency, the depth of the trajectory below the average level of the potential energy landscape. Motivation is seen as the potential of an organism for motion under the forces impinging on it. Degree of motivation is characterized by the depth of the potential field, with l...

Huge databases are being used in organizations to store data. These databases contain hidden patterns which can be discovered and used in the organizations. In this project, we applied data mining techniques to uncover the patterns in the circulation database of UTM library. In order to discover worthwhile patterns we followed knowledge discovery process (KDD) to transform row data to suitable format. Weka machine learning software was applied to do the data mining task. In this project, we studied two association rules mining algorithms, Apriori and FPGrowth. The later was used to discover some patterns among borrowed books. These patterns which are presented in a list can be used to make recommendations to patrons who are searching for a certain topic based on items that previously were borrowed together. In addition, a novel rule matrix was presented to store the found rules for future use. Both the list for recommendation and rule matrix are useful to construct a recommender sys...

Aggressive signaling is a key social behavior of male Siamese fighting fish (Betta splendens). Successfully establishing a territory and defending it from intruders has direct fitness effects, making Betta splendens a prime model for studies examining the biological underpinnings of aggressive behavior. Current research has outlined serotonin transporter pathways as one key component for the engagement and coordination of aggressive behavior in Betta splendens. Using the selective serotonin reuptake inhibitor fluoxetine, we examined the impact of 10μmol exposures on associative learning and aggression between mirror and conspecific social reinforcers. Our results provide clear evidence that exposure to fluoxetine reduces aggression and impairs learning independent of social reinforce type. In addition, our results provide support for motor inhibition of aggressive behavior as the main behavioral mechanism of action for fluoxetine. Placed within the broader context of behavioral synd...

In data mining, association rule mining is a popular and well researched method for discovering interesting relations between variables in large databases, which are meaningful to the users and can generate strong rules on the basis of these frequent patterns, which are helpful in decision support system. Quantum Particle Swarm Optimization (QPSO) is one of the several methods for mining association rules. It combines the aspects of traditional PSO philosophy and quantum mechanics. However, preventing the occurrence of local optima and improving the convergence speed is still a tedious task. In this paper, an Evolutionary Quantum behaved Particle Swarm Optimization (EQPSO) is presented with improved computational efficiency and has proper convergence. The proposed work introduces local search techniques into QPSO using Modified Shuffled Frog Leaping Algorithm (MSFLA) and depicts a systematic parameter adaptation by developing an Evolutionary State Estimation (ESE) and an Elitist Learning Strategy (ELS). The EQPSO implementation has comprehensively been evaluated on 5 different datasets taken up from the UCI Irvine repository. The performance of EQPSO is compared with Basic QPSO and the experimental results shows that the proposed system outperforms the existing algorithm quite significantly.

With the exception of honeybees, there have been few good invertebrate models for associative learning. Grasshoppers and locusts (Orthoptera: Acrididae) possess a number of characteristics that make them excellent candidates for such studies, and in this paper we present a novel protocol, based on a Y-maze, that is specifically designed for studying their learning and choice behaviour. Three separate experiments were conducted using individual gregarious forms of the desert locust, Schistocerca gregaria. In our first experiment, coloured arms of a two-sided Y-maze provided a large or small amount of wheat for nine choice-trials. In the second experiment, locusts discriminated odours with wheat rewards for nine choice-trials. The odour-wheat reward combinations were then reversed for an additional nine choice-trials. For the third experiment, the locusts again discriminated odours, but here we used artificial foods and the rewards differed in their concentration of protein and digestible carbohydrate. The results indicate that, in addition to showing good acquisition of choice performance, the locusts also took less time to reach the larger-rewarded option. The data indicate that our protocol is highly sensitive for recording choice behaviour in acridids and reveals the potential they have for advancing our current understanding of associative learning and the field of learning in general.

We propose that the isomorphism generally observed between the representations composing our momentary phenomenal experience and the structure of the world is the end-product of a progressive organization that emerges thanks to elementary associative processes that take our conscious representations themselves as the stuff on which they operate, a thesis that we summarize in the concept of Self-Organizing Consciousness (SOC).

The COVID-19 pandemic has led to an increase in digitization. With the strict social and physical distancing measures in place, new routines require accessing the internet for most online services which have led to the explosive growth of data. As a consequence, data mining technologies are used for the extraction of useful information from a huge compilation of such digital data. Thus, the desire to mine data from varied sources to discover behaviors and patterns among entities such as customers, diseases, and environmental conditions is on the rise which can be accomplished by association rule mining. However, such pattern discovery by association rule mining also discloses the personal information of an individual or organization. Thus, the challenge of association rule mining is privacy preservation wherein confidentiality of sensitive rules should be maintained while releasing the database of third parties. Privacy-preserving association rule mining is the process of modifying the original database to hide the sensitive rules for preserving privacy. Thus, the paper emphasizes multiple objectives like minimizing the side effects of hiding sensitive rules. i.e. reduce the number of ghost rules, lost rules, and hiding failure along with the increase in utility of the data.

With technological revolution, a huge amount of data is being collected and as a consequence the need of mining knowledge from this data is triggered. But, data in its raw form comprises of sensitive information and advances in data mining techniques have increased the privacy breach. However, due to socio-technical transformations, most countries have levied the guidelines and policies for publishing certain data. As a result, a new area known as Privacy Preserving Data Mining (PPDM) has emerged. The goal of PPDM is to extract valuable information from data while retaining privacy of this data. The paper focuses on exploring PPDM in different aspects, such as types of privacy, PPDM scenarios and applications, methods of evaluating PPDM algorithms etc. Also, the paper shows parametric analysis and comparison of different PPDM techniques. The goal of this study is to facilitate better understanding of these PPDM techniques and boost fruitful research in this direction.

The purpose of this paper is to propose a combined data mining approach for analyzing and profiling customers in video on demand (VoD) services. The proposed approach integrates clustering and association rule mining. For customer segmentation, the LRFMP model is employed alongside the k-means and Apriori algorithms to generate association rules between the identified customer groups and content genres. The applicability of the proposed approach is demonstrated on real-world data obtained from an Internet protocol television (IPTV) operator. In this way, four main customer groups are identified: ''high consuming-valuable subscribers'','' less consuming subscribers'','' less consuming-loyal subscribers'' and ''disloyal subscribers''. In detail, for each group of customers, a different marketing strategy or action is proposed, mainly campaigns, special-day promotions, discounted materials, offering favorite content, etc. Further, genres preferred by these customer segments are extracted using the Apriori algorithm. The results obtained from this case study also show that the proposed approach provides an efficient tool to form different customer segments with specific content rental characteristics, and to generate useful association rules for these distinct groups. The proposed combined approach in this research would be beneficial for IPTV service providers to implement effective CRM and customer-based marketing strategies.

Reinforcer magnitude and fixed‐ratio requirement were varied under two second‐order schedules. Under one, the first sequence of a fixed number of responses completed after the lapse of a 10‐min fixed interval produced reinforcement. Under the second, a second‐order progressive‐ratio schedule, the fixed number of responses increased after each reinforcement. Either cocaine (0 to 300 μg/kg/inj) or food (0 to 5,700 mg/delivery) reinforcers were delivered. Under some conditions, a 2‐s illumination of stimulus lights occurred on completion of each ratio sequence. Under the second‐order schedule, as cocaine dose or amount of food increased, rates of responding increased; at the highest values, rates of responding decreased. Increases in the ratio requirement from 10 to 170 responses minimally decreased overall response rates. Under the second‐order progressive‐ratio schedule, increases in dose of cocaine or amount of food increased rates of responding; at the highest amounts of food, rate...

Steganography stands as a prominent technique, allowing the covert embedding of secret data within seemingly innocuous files. This project introduces a comprehensive approach to multi-format steganography, enabling the concealment of data within images, audio, video, and text files. The system employs the efficient RC4 encryption algorithm, chosen for its simplicity, speed, and adaptability in various key sizes. Through a command-line interface and leveraging Python libraries such as NumPy, Pandas, OS, and Cv2, the project encompasses distinct modules catering to different file types. This research work bridges the gap between flexibility and security, offering a high performance, multi-format steganography tool. Its implementation not only demonstrates the practicality of data concealment across varied files but also sheds light on potential areas for further optimization and enhancement. This research fills a critical void by providing a comprehensive tool that balances flexibility and security, offering high-performance multiformat steganography capabilities. Its implementation not only showcases the practicality of concealing data across diverse file formats but also highlights potential avenues for further optimization and enhancement.

In recent years, the concept of temporal association rule (TAR) has been introduced in order to solve the problem on handling time series by including time expressions into association rules. In real life situations, temporal databases are often appended or ...