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iLIAC: An approach of identifying dissimilar groups on unstructured numerical image dataset using improved agglomerative clustering technique

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Multimedia Tools and Applications 2024年第39期83卷 86359-86381页

作者： S, Sreedhar Kumar Ahmed, Syed Thouheed Fathima, Afifa Salsabil Mathivanan, Sandeep Kumar Jayagopal, Prabhu Saif, Abdu Gupta, Sachin Kumar Sinha, Garima Department of Computer Science and Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electrical Engineering Indian Institute of Technology Hyderabad Telangana Kandi India School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University Taiz Yemen Katra India Department of Computer Science Engineering Jain University Bangalore India

Unstructured Numerical Image Dataset Separation (UNIDS) method employing an enhanced unsupervised clustering technique. The objective is to delineate an optimal number of distinct groups within the input grayscale (G-S) image content, utilizing the improved limited iteration agglomerative clustering (iLIAC) clustering technique for the separation and enhancement of picture elements. The UNIDS method is structured into two primary stages: partitioning and validation. In the partitioning stage, the UNIDS method identifies an appropriate number of discrete clusters within the grayscale image using the iLIAC technique, eliminating the need for predetermined procedures. Subsequently, the method evaluates the similarity and deviation among data elements within the same group in the resultant image dataset. Additionally, it assesses the proximity and inters severance among clusters in the outcome of the image dataset through the partitioning process. Empirical results indicate that the UNIDS system excels in the spontaneous identification of an optimal number of discrete clusters within the input G-S image. The system demonstrates superior thickness, reduced deviation among data elements within the same cluster, increased inter-separation, and diminished inter-closeness between cluster elements. Furthermore, empirical analysis establishes the superior performance of the UNIDS approach compared to existing clustering techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Iterative methods

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Prediction of strength properties of concrete under the influence of recycled aggregate using machine learning models

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Interactions 2024年第1期245卷 1-21页

作者： Ashwathi, R. Soundariya, R.S. Tharsanee, R.M. Yuvaraj, S. Ramya, R. Department of Civil Engineering Bannari Amman Institute of Technology Erode India Department of Computer Technology Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Civil Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Information Technology Dr NGP Institute of Technology Coimbatore India

The upsurge in urbanization is depleting natural resources due to the wide usage of aggregates in construction, posing a menace to further progress. If the contemporary state persists, it becomes a threat for further progress. As an initiative to preserve the natural resources, recycled aggregate is employed as a partial additive of fine aggregate, this practice endorses sustainability but demands a cautious investigation of its effect on concrete strength. Predicting the strength characteristics of concrete becomes crucial in this situation. Machine Learning (ML) algorithms estimate these properties, enabling engineers to evaluate and enhance the concrete mix with recycled materials, confirming durability and performance without solely depending on outdated testing methods The proposed work attempts how ML algorithms have been utilized to forecast the strength properties of concrete. Input samples were collected from the experimental setup for three grades of concrete (M15, M20, M25) with four various proportions (5%, 10%, 15% and 20%) at 7, 14, 21 and 28 ages of curing excluding the conventional mix. The ML models are developed by training the collected laboratory samples with respect to four parameters– cement, fine aggregate (FA), coarse aggregate (CA) and recycled aggregate (RA). The trained models predict the three properties of concrete– compressive strength, split tensile strength and flexural strength. The test outcomes are further validated using statistical measures such as correlation coefficient– R2, Mean Square Error (MSE) and Mean Absolute Error (MAE) to assess the performance of the model. The performance evaluation of the proposed approach shows that The Support Vector Regressor outperforms the other ML models, achieving an R² value of about 90% and showing a reduced error rate in terms of MSE and MAE. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Tensile testing

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CARM:Context Based Association Rule Mining for Conventional Data

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computers, Materials & Continua 2021年第9期68卷 3305-3322页

作者： Muhammad Shaheen Umair Abdullah Faculty of Engineering&Information Technology Foundation UniversityIslamabadPakistan Department of Computer Science Barani Institute of Information TechnologyRawalpindiPakistan

This paper is aimed to develop an algorithm for extracting association rules,called Context-Based Association Rule Mining algorithm(CARM),which can be regarded as an extension of the Context-Based Positive and Negative Association Rule Mining algorithm(CBPNARM).CBPNARM was developed to extract positive and negative association rules from Spatiotemporal(space-time)data only,while the proposed algorithm can be applied to both spatial and non-spatial *** proposed algorithm is applied to the energy dataset to classify a country’s energy development by uncovering the enthralling interdependencies between the set of variables to get positive and negative *** association rules related to sustainable energy development are extracted by the proposed algorithm that needs to be pruned by some pruning *** context,in this paper serves as a pruning measure to extract pertinent association rules from non-spatial *** Probability Increment Ratio(CPIR)is also added in the proposed algorithm that was not used in *** inclusion of the context variable and CPIR resulted in fewer rules and improved robustness and ease of ***,the extraction of a common negative frequent itemset in CARM is different from that of *** rules created by the proposed algorithm are more meaningful,significant,relevant and *** accuracy of the proposed algorithm is compared with the Apriori,PNARM and CBPNARM *** results demonstrated enhanced accuracy,relevance and timeliness.

关键词： Association rules context CBPNARM non-spatial data CPIR support confidence interestingness

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Predicting Student Behavior Using a Neutrosophic Deep Learning Model

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Neutrosophic Sets and Systems 2025年 76卷 288-310页

作者： Shitaya, Ahmed Mohamed Wahed, Mohamed El Syed Abd El Khalek, Saied Helemy Ismail, Amr Shams, Mahmoud Y. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Computer Science Faculty of computer and information sciences Suez Canal University Egypt Department. of Communication and Computer Engineering Air Defense University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt

We developed an information system using an object-oriented programming language and a distributed database (DDB) consisting of multiple interconnected databases across a computer network, managed by a distributed database management system (DDBMS) for easy access. An intelligent system was designed to assess the difficulty level of preliminary exams and select top-performing advanced students using a Neutrosophic Deep Learning Model. The dataset was randomly split into training (80%) and testing (20%) sets, and the model, trained with the Adam optimizer at a 0.001 learning rate over 50 epochs, incorporated early stopping based on validation loss. This system, implemented at a traditional Egyptian university, achieved a 95% accuracy in predicting student dropout. Student behavior, influenced by personal, environmental, and academic factors, is often evaluated subjectively, leading to inconsistent results. Traditional machine learning approaches struggle with the inherent uncertainty in behavioral data. To address this, we combined neutrosophic theory—a mathematical framework that accounts for truth, falsity, and indeterminacy—with deep learning, which excels at learning complex data relationships, to predict student outcomes such as dropout rates. Evaluating the model on student data, including attendance and grades, showed superior accuracy, achieving a determination coefficient of 0.95, demonstrating the approach's potential for identifying at-risk students and enabling targeted interventions. © 2025, University of New Mexico. All rights reserved.

关键词： Contrastive Learning

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Next-k Items Recommendation with Item Novelty 14th

Next-k Items Recommendation with Item Novelty

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14th International Conference on Frontier Computing, FC 2024

作者： Ma, Hao-Shang Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan

ISBN: (纸本)9789819623945

Recommendation engines leverage past user preferences to forecast their future interests. Many deep learning-based recommendation systems aim to explore the intricate dynamics between users and particular items. Typically, the most recommendation systems have a tendency to suggest items based on their popularity or relevance to the user’s previous interactions, which will lead to a narrow range of recommendations. We would like to tackle this issue by evaluating potential items based on the novelty concept, effectively broadening the spectrum of recommendations. Furthermore, we detail a framework that allows for the seamless integration of this novel feature into next-k item recommendation architectures. Through rigorous experiments, the proposed method demonstrated superior performance compared with existing methods, confirming that integrating a novelty score can significantly refine the quality of recommendations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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DNA Computing with Water Strider Based Vector Quantization for Data Storage Systems

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computers, Materials & Continua 2023年第3期74卷 6429-6444页

作者： A.Arokiaraj Jovith S.Rama Sree Gudikandhula Narasimha Rao K.Vijaya Kumar Woong Cho Gyanendra Prasad Joshi Sung Won Kim Department of Networking and Communications College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science Engineering Aditya Engineering CollegeSurampalemAndhra Pradesh533437India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of *** accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of *** quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression *** article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for Data Storage *** proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data ***,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded *** addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably *** order to generate optimal codebook for LBG,the WSA is applied to *** performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several *** comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.

关键词： DNA computing data storage image compression vector quantization ws algorithm space saving

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TISCMB: design of a highly efficient blockchain consensus model with trust integrated self-correcting miner selection

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International Journal of information technology (Singapore) 2023年第4期15卷 1845-1858页

作者： Ravi Kumar, Shipra Goyal, Mukta Department of Computer Science Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Blockchain is rapidly becoming the de facto standard for storage applications requiring high transparency, record traceability, immutable data, and distributed processing. Researchers have proposed a large number of such models, including Proof-of-Work (PoW), Proof-of-Stake (PoS), Proof-of-Authority (PoA), etc. Due to their respective limitations, each of these models is applied to context-specific blockchain deployments. In addition, the selection of the most efficient miners for hash calculation and verification is a complex task that must be executed with high efficiency in order to improve network performance. The novel contribution of this work is to design a hybrid consensus model which uses a combination of Proof-of-Work and Proof-of-Stake consensus methods for fast hash computations. Proposed model is supported by a highly efficient trust-based miner selection method that aids in choosing the most optimal miner nodes with low processing delay and high energy efficiency. In addition, this text proposes a self-correcting mechanism for the designed blockchain, which assists in the blockchain's correction in the event of any internal or external attacks. Due to these characteristics, it is observed that the proposed model has 20% less delay, 8% less energy consumption, and 15% higher levels of trust than standard PoS and PoW consensus models. The model was also subjected to various attack scenarios, and it was determined that it is capable of self-correcting the node's internal blockchain with a 99.9% success rate, thereby enhancing its real-time deployment capabilities. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Blockchain Consensus Delay Energy Self-correcting Trust

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Escape Routing Using Deep Reinforcement Learning

Escape Routing Using Deep Reinforcement Learning

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2025 International Workshop on Advanced Imaging technology, IWAIT 2025

作者： Cheng, Hsu Fu Cheng, Dun Wei Department of Computer Science and Information Engineering National Yunlin University of Science & Technology Yunlin Taiwan

ISBN: (数字)9781510688131

ISBN: (纸本)9781510688124

Escape routing is a critical task in the design of printed circuit boards (PCB) and integrated circuits (IC), aiming to establish effective connections among multiple pin points while avoiding path overlaps and interference from *** paper proposes a Deep Reinforcement Learning (DRL)-based solution utilizing a Deep Q-Network (DQN) to address the escape routing *** problem is modeled as a Markov Decision Process (MDP), and the agent learns effective routing strategies through interactions with the *** were conducted in a 25×26 simulated grid environment, testing scenarios with 30, 60, and 90 pin *** study highlights the potential of Deep Reinforcement Learning in solving escape routing problems, offering a novel approach to addressing routing challenges in PCB design. © 2024 SPIE.

关键词： Deep reinforcement learning

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Optimizing Technical Analysis and Stock Price Analysis Using: BLRM-QNDNL 21

Optimizing Technical Analysis and Stock Price Analysis Using...

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21st OITS International Conference on information technology, OCIT 2023

作者： Kalva, Sudhakar Naganjaneyulu, Satuluri Sudhakar, K. Computer Science and Engineering Department of Information Technology Department of Computer Science and Engineering Kurnool India

ISBN: (纸本)9798350358230

The Stock value forecast is a significant issue to determine the future direction of the financial Markets. Many research works are carried out and design many techniques to predict stock price of Individual stocks. But, the forecast precision/ of the regular methods was lower when taking the enormous size of the dataset. To address this downside, a Novel Deep Learning based Broken-Stick Linear Regressive MapReduce Based on Quasi-Newton Deep Neural Learning (BLRM-QNDNL) method is proposed right now. BLRM-QNDNL technique targeting optimizing the parameters engaged with stock profit prediction. Quasi-Newton based Deep Neural Network Learning (Q-NDNL) is applied in the BLRM-QNDNL procedure for precisely foreseeing the stock prices dependent on the news with higher accuracy. Q-NDNL used several hidden layer in order to thoroughly examine the effects of emotions conveyed in news items on stocks with the least amount of time complexity. The BLRM-QNDNL method expands the stock price forecast performance with lower time complexity when compared with cutting edge works. The BLRM-QNDNL technique has been evaluated exploratory on metrics such as prediction accuracy, prediction time, and false positive rate with regard to different numbers of data. When compared to state-of-the-art studies, the experimental results show that the BLRM-QNDNL technique can reduce the time complexity of stock price prediction while simultaneously increasing its accuracy. The suggested BLRM-QNDNL technique reduces the false positive stock price prediction rate by 41% and 57%, respectively and decreases market price prediction time complexity by 9 percent and 29 percent relative to traditional DeepClue [1] and MFNN [2], respectively. © 2023 IEEE.

关键词： MapReduce

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Preserving friendly stacking and weighted shadows in selective scalable secret image sharing

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Multimedia Tools and Applications 2023年第19期82卷 29283-29305页

作者： Lee, Jung-San Chen, Yin-Chin Hsieh, Yun-Hao Chang, Shih-Hao Huynh, Ngoc-Tu Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan Department of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam

With advancement of technology, the technique of selective scalable secret image sharing (SSSIS) was first proposed in 2017 as a new alternative of secret image sharing. The region of interest (ROI) in the protected secret could be revealed progressively once the number of collected shadows has reached a predefined threshold. Accordingly, the ROIs of the secret image increase based on the number of collected shadows. Nonetheless, players without sufficient shadows may be able to learn the location and contour of hidden secret to perform advanced guessing attacks. Thus, we aim to introduce friendly stacking to yield a genuine outcome of mitigating this risk during the recovery process. Furthermore, the weighting concept is brought into constructing the meaningful shadow so that the priority of a specific participant can be emphasized to approach real applications. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Image segmentation

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