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Enhancing Student Performance for Low Rank Students using Adaptive Deep Multi-Perception pattern learning Classification Model

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SN Computer Science 2025年第1期6卷 1-13页

作者： Ali, S. Rasheed Mansoor Sundravadivelu, K. Muthukumar, S. Ibrahim, S. Syed Department of Computer Science Jamal Mohamed College (Autonomous) (Affiliated to Bharathidasan University) Tamilnadu Tiruchirappalli India Department of Computer Science Madurai Kamaraj University Tamil Nadu Madurai India Department of Computer Science PVP College K. Singarakottai Tamilnadu Dindugul India

The tendency of student ability is important for knowledge improvement based on the quality of student learning skills. In recent, the educational services are affected by COVID-19 in continues pandemic period to train the students. There is difficult to process the student features which its leads large collective data forums in unstructured principle. Moreover the large number features creates high dimensions due to improper assets in student information along with grades, performance activities, curriculum etc. so the essential thing is feature based dimension reduction, because the existing prediction model failed to observe the feature weights. To overcome the issues, the novelty creates an adaptive prediction model to classify the results. To propose a Habitual Impact scaling feature selection based student performance prediction via adaptive Deep Multi Perception pattern learning Classification (DMP²LC) model. Initially this pre-process take place to reduce the noise and non-redundant date reduction to formalize the dataset which is collected from college academic student features dataset. Then features weight scaling factors are verified by Threshold margins to get the structural data. Then the student interest successive rate (SISR) weight are observed by the threshold values by habitual interest of the student learning. Each threshold SISR is scaled into sub-spectral clustering weight (S²CW) to group the important features related to academic attribute, then other margins are neglected to reduce the dimension. Then the Subjective pattern sequence rate (SPSR) is generated to predict the correlation features. Finally the SPSR is trained into Deep Multi Perception pattern learning Classification (DMP²LC) model to classify the result into categorized classes. From the result low rank predicted students get high preference to motivate to improve the academic performance. This DMP²LC high performance in contrast to the

关键词： Deep learning Feature selection and classification Habitual analysis pattern learning Student performance Sub-spectral clustering

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pattern learning Based Parallel Ant Colony Optimization 15

Pattern Learning Based Parallel Ant Colony Optimization

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15th IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) / 16th IEEE International Conference on Ubiquitous Computing and Communications (IUCC)

作者： Jin, Xiaotian Zheng, Wenbo Mo, Shaocong Qu, Yili Jin, Xin Zhou, Jiangwei Duan, Pengfei Zheng, Tao Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430070 Hubei Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Shaanxi Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Guangdong Peoples R China Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Hubei Peoples R China Wuhan Univ Technol Hubei Key Lab Transportat Internet Things Wuhan 430070 Hubei Peoples R China Wuhan Univ Technol Sch Foreign Languages Wuhan 430070 Hubei Peoples R China

ISBN: (纸本)9781538637906

Ant colony optimization (ACO) can be used to solve complex optimization problems in engineering, economic management and military strategy. Most of these are NP hard problems, which are difficult to solve with traditional methods. An improved parallel ACO algorithm based on pattern learning is proposed in this paper. It extracts parameters automatically to reduce solution space and enhance calculation efficiency. Various parameters in the algorithm are analyzed, and a refining strategy is formed according to ACOs characteristics. The parallel ACO algorithm is carried out under the MIC/CPU architecture, and it can significantly enhance performance.

关键词： pattern learning Ant Colony Optimization MIC

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Suppressing detection of inconsistency hazards with pattern learning

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INFORMATION AND SOFTWARE TECHNOLOGY 2016年第0期74卷 219-229页

作者： Xi, Wang Xu, Chang Yang, Wenhua Ma, Xiaoxing Yu, Ping Lu, Jian Nanjing Univ State Key Lab Novel Software Technol Nanjing 210008 Jiangsu Peoples R China Nanjing Univ Dept Comp Sci & Technol Nanjing 210008 Jiangsu Peoples R China

Context: Inconsistency detection and resolution is critical for context-aware applications to ensure their normal execution. Contexts, which refer to pieces of environmental information used by applications, are checked against consistency constraints for potential errors. However, not all detected inconsistencies are caused by real context problems. Instead, they might be triggered by improper checking timing. Such inconsistencies are ephemeral and usually harmless. Their detection and resolution is unnecessary, and may even be detrimental. We name them inconsistency hazards. Objective: Inconsistency hazards should be prevented from being detected or resolved, but it is not straightforward since their occurrences resemble real inconsistencies. In this article, we present SHAP, a pattern learning based approach to suppressing the detection of such hazards automatically. Method: Our key insight is that detection of inconsistency hazards is subject to certain patterns of context changes. Although such patterns can be difficult to specify manually, they may be learned effectively with data mining techniques. With these patterns, we can reasonably schedule inconsistency detections. Results: The experimental results show that SHAP can effectively suppress the detection of most inconsistency hazards (over 90%) with negligible overhead. Conclusions: Comparing with other approaches, our approach can effectively suppress the detection of inconsistency hazards, and at the same time allow real inconsistencies to be detected and resolved timely. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Context inconsistency Inconsistency hazard pattern learning

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PRIMITIVE pattern learning

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pattern RECOGNITION 1992年第8期25卷 883-889页

作者： CHAN, TYT GOLDFARB, L UNIV NEW BRUNSWICK FAC COMP SCIBOX 4400FREDERICTON E3B 5A3NBCANADA INTELLIGENT INFORMAT SYST FREDERICTON E3B 2V1NBCANADA

A new approach to the feature detection problem, i.e. learning "useful" primitive features from raw images, is proposed. The "useful" features are defined within the training environment as those that allow the learning agent (learning system) to form object representations sufficient for subsequent object recognition. In other words, the "useful" features detected are discriminating, useful features.

关键词： PRIMITIVE IMAGE FEATURES pattern learning MACHINE VISION LOCAL DISPARITY FUNCTION

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Auditory temporal pattern learning by songbirds using maximal stimulus diversity and minimal repetition

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ANIMAL COGNITION 2014年第5期17卷 1023-1030页

作者： Comins, Jordan A. Gentner, Timothy Q. Univ Calif San Diego Dept Psychol La Jolla CA 92092 USA Ctr Acad Res & Training Anthropogeny La Jolla CA USA Univ Calif San Diego Neurosci Program La Jolla CA 92092 USA Kavli Inst Brain & Mind La Jolla CA USA

The sequential patterning of complex acoustic elements is a salient feature of bird song and other forms of vocal communication. For European starlings (Sturnus vulgaris), a songbird species, individual vocal recognition is improved when the temporal organization of song components (called motifs) follows the normal patterns of each singer. This sensitivity to natural motif sequences may underlie observations that starlings can also learn more complex, unnatural motif patterns. Alternatively, it has been proposed that the apparent acquisition of abstract motif patterning rules instead reflects idiosyncrasies of the training conditions used in prior experiments. That is, that motif patterns are learned not by recognizing differences in temporal structures between patterns, but by identifying serendipitous features (e.g., acoustical cues) in the small sets of training and testing stimuli used. Here, we investigate this possibility, by asking whether starlings can learn to discriminate between two arbitrary motif patterns, when unique examples of each pattern are presented on every trial. Our results demonstrate that abstract motif patterning rules can be acquired from trial-unique stimuli and suggest that such training leads to better pattern generalization compared with training with much smaller stimulus subsets.

关键词： Vocal recognition pattern learning Auditory cognition Trial-unique stimuli

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Precedence of spatial pattern learning revealed by immediate reversal performance

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BEHAVIOURAL PROCESSES 2010年第3期85卷 252-264页

作者： Brown, Michael F. Sturz, Bradley R. Andriole, Katherine Hardesty, Shaina Place, Margaret Villanova Univ Dept Psychol Villanova PA 19085 USA Armstrong Atlantic State Univ Savannah GA 31419 USA

Human participants learned to choose eight correct locations in a 4 x 4 matrix on a computer display. The locations were arranged either in a structured spatial pattern or an unstructured but consistent spatial arrangement. When the assignment of correct and incorrect locations was reversed after initial learning, participants in the spatial pattern condition demonstrated reversal performance immediately (i.e., following the first choice after reversal of the contingencies). Follow-up experiments confirmed that immediate reversal performance depends on a structured spatial pattern among the locations and that a learned motor pattern cannot explain the immediate reversal performance. This pattern of results shows that learning the spatial relations among locations has precedence over learning about the individual locations, even when the individual locations are completely valid predictive cues. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Spatial cognition pattern learning Reversal learning Equivalence class

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Relation Extraction: Hypernymy Discovery Using a Novel pattern learning Algorithm

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SN Computer Science 2023年第6期4卷 730页

作者： Pinto, Olan Gole, Shraddha Srushti, H.P. Madasamy, Anand Kumar Department of Information Technology National Institute of Technology Karnataka Karnataka Surathkal 575025 India

This paper proposes a semi-supervised relation extraction methodology to extract hypernymy (Is-A) relations. We developed a pattern learning-based model based on a "most reliable pattern". After each iteration, the algorithm generates trusted instances of hypernym–hyponym pairs using only a corpus of text and a set of seed instances as the input. Sentences are masked and extracted, and patterns are discovered and ranked. A pattern-matching algorithm generates pairs, and a scoring function appropriately filters pairs. The generated pairs are added to the initial seed set via a bootstrapping approach to facilitate further the iterative algorithm in generating a new trusted pair set. The work presented here is a semi-supervised approach, and to facilitate the experiments conducted, we are using two freely available public Wikipedia text corpus to extract hypernyms. We use Hearst patterns, an extended version of Hearst patterns (adding more patterns), and a dependency-based approach to form a base for comparison to our developed pattern learning approach. To evaluate the proposed algorithm, the hypernym–hyponym relations obtained are tested against five standard publicly available datasets, namely, BLESS, WBLESS, WEEDS, EVAL, and LEDS datasets as criteria for comparison. The results of the two Wikipedia text corpus and five evaluation datasets show that the pattern learning approach performs better than the three comparison base algorithms. The lack of heavy skewness in results across the two datasets also indicates that the algorithms implemented are independent of the corpus used and can be used on any large corpus. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Dependency relations Hearst patterns Hypernym Hyponym Natural language processing pattern learning Relation extraction

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Model Predictive Control with pattern learning of Prediction and Control Trajectory and its Application to a Battery EMS Problem 60

Model Predictive Control with Pattern Learning of Prediction...

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60th Annual Conference of the Society-of-Instrument-and-Control-Engineers-of-Japan (SICE)

作者： Iino, Yutaka Hayashi, Yasuhiro Waseda Univ ACROSS Tokyo Japan

ISBN: (纸本)9784907764739

While the model predictive control method can be widely applied to energy systems and industrial fields, it is difficult to evaluate the reproducibility in advance, and there is a limit to the quality assurance of the control system. In this paper, we propose the prediction / control trajectory of the model predictive control as a control parameter and tried to simplify the model predictive control algorithm by limiting it to a finite number of scenario patterns using the k-means clustering method. This approach does not require optimization calculations and can reduce the computational load and ensure the reproducibility of control operations. The proposed method was applied to the energy management system (EMS) problem with battery storage. The target is an energy system consisting of fluctuating demand load and photovoltaic power generation as a variable renewable energy resource, and storage battery and commercial power reception, and the proposed method is applied to the optimization of storage battery operation. Focusing on the net load pattern as the predicted trajectory and the storage battery's state of charge (SOC) pattern as the control trajectory, the proposed pattern learning model predictive control EMS method was applied to each pattern. The former corresponds to output feedback control and the latter corresponds to state feedback control. Also, state observer-based control is considered where the state is estimated by neural network. As a result of evaluation by simulation of EMS operation for one year based on actual data of PV power generation and demand, control loss increase of only about 8% compared to the optimal control case, with only 3 representative patterns of prediction and control trajectory parameters. So, the effectiveness of the proposed method was confirmed. We also confirmed the robustness against demand forecast error and representative pattern selection error.

关键词： model predictive control pattern learning k-means clustering EMS SOC pattern of battery storage

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SPARSE NON-NEGATIVE pattern learning FOR IMAGE REPRESENTATION

SPARSE NON-NEGATIVE PATTERN LEARNING FOR IMAGE REPRESENTATIO...

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15th IEEE International Conference on Image Processing (ICIP 2008)

作者： Gong, Dian Zhao, Xuemei Yang, Qiong Univ Calif Riverside Dept Elect Engn Riverside CA 92521 USA Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Katholieke Univ Leuven ESATPSI VISICS B-3001 Heverlee Belgium

ISBN: (纸本)9781424417650

In this paper, we propose Sparse Non-negative pattern learning (SNPL) based on self-taught learning framework. In the algorithm, visual patterns are first learned from unlabeled data by non-negative matrix approximation with sparseness constraints, and then features are extracted by the second part of the algorithm, a conjugate family based non-negative sparse feature extraction method. By combining sparse and non-negative constraints of patterns together, SNPL model gives a better representation for images than state-of-art methods. Beyond that, we give an analytical solution for feature extraction although it is approximate, and thereby we extract the features for self-taught learning framework in a faster and more stable way. We apply the new model to various areas, including pattern coding, feature extraction, and recognition. Experimental results show the advantages of SNPL model.

关键词： Self learning Non-negative Matrix Approximation pattern learning Sparse Representation Feature Extraction

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Memristors' Potential for Multi-bit Storage and pattern learning 9

Memristors' Potential for Multi-bit Storage and Pattern Lear...

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UKSim-AMSS 9th IEEE European Modelling Symposium on Computer Modelling and Simulation (EMS)

作者： TaheriNejad, Nima Manoj, Sai P. D. Jantsch, Axel Vienna Univ Technol Fac Elect Engn & Informat Technol Inst Comp Technol Gusshausstr 27-29 A-1040 Vienna Austria

ISBN: (纸本)9781509002061

Memristor is a two-terminal device, termed as fourth element, and characterized by a varying resistance depending on the charge (current) flown through it. This leads to many interesting characteristics, including a memory of its past states, demonstrated in its resistance. Smaller area and power consumed by memristors compared to conventional memories makes them a more suitable choice for applications needing large memory. In this paper we explore one of the unique properties of memristors which extends their suitability by allowing storage of multi-bit data in a single memristor. Their ability of storing multi-bit patterns will be shown via a simplified proof and simulations. This characteristic can be advantageous for many applications. In this paper particularly, we briefly discuss its advantages in pattern learning applications.

关键词： Memristors Multi-bit Storage pattern learning pattern Recognition Precision Digital Coding Systems

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