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Knowledge-Driven Possibilistic Clustering with Automatic Cluster Elimination

computers, Materials & Continua 2024年第9期80卷 4917-4945页

作者： Xianghui Hu Yiming Tang Witold Pedrycz Jiuchuan Jiang Yichuan Jiang The School of Computer Science and Engineering Southeast UniversityNanjing211189China The School of Computer and Information Hefei University of TechnologyHefei230601China The Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R 2V4Canada The Systems Research Institute Polish Academy of SciencesWarsaw00-901Poland The School of Information Engineering Nanjing University of Finance and EconomicsNanjing210023China

Traditional Fuzzy C-Means(FCM)and Possibilistic C-Means(PCM)clustering algorithms are data-driven,and their objective function minimization process is based on the available numeric ***,knowledge hints have been introduced to formknowledge-driven clustering algorithms,which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge ***,these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself;they require the assistance of evaluation ***,knowledge hints are usually used as part of the data structure(directly replacing some clustering centers),which severely limits the flexibility of the algorithm and can lead to *** solve this problem,this study designs a newknowledge-driven clustering algorithmcalled the PCM clusteringwith High-density Points(HP-PCM),in which domain knowledge is represented in the form of so-called high-density ***,a newdatadensitycalculation function is *** Density Knowledge Points Extraction(DKPE)method is established to filter out high-density points from the dataset to form knowledge ***,these hints are incorporated into the PCM objective function so that the clustering algorithm is guided by high-density points to discover the natural data ***,the initial number of clusters is set to be greater than the true one based on the number of knowledge ***,the HP-PCM algorithm automatically determines the final number of clusters during the clustering process by considering the cluster elimination *** experimental studies,including some comparative analyses,the results highlight the effectiveness of the proposed algorithm,such as the increased success rate in clustering,the ability to determine the optimal cluster number,and the faster convergence speed.

关键词： Fuzzy C-Means(FCM) possibilistic clustering optimal number of clusters knowledge-driven machine learning fuzzy logic

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A robust method for malware analysis using stacking classifiers and dendrogram visualization

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International Journal of Information technology (Singapore) 2024年 1-16页

作者： Naveen Kumar, N. Balamurugan, S. Maruthamuthu, R. Devi, P.P. Seshu Kumar, P. Department of Computer Applications Madanapalle Institute of Technology & amp Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Avadi Tamil Nadu Chennai India

Malware analysis is a vital and challenging task in the ever-changing cyber threat landscape. Traditional signature-based methods cannot keep up with the fast-paced evolution of malware variants. This underscores the need for developing a more effective malware classification tool helping in exploring new research directions with solutions. In this research work firstly, we present a comprehensive assessment on malware classification using dendrogram clustering techniques. This work proposes, a robust approach to malware analysis by constructing dendrogram that groups malware samples which works based on their ancestral relationships. Semantic analysis of the code also provides deeper insights into the malware’s behavior and functionality, enabling more precise identification. These methods help to detect new variants as well as enhance the recognition of existing malware families. Our proposed model results shows that, the robustness in accuracy and False Positive Rate when compared with other existing models. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Autoencoder decoder Dendrogram Malware classification Semantic analysis

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Holistic Knowledge Framework for Improving PC Design using Big Data Analytics 7

Holistic Knowledge Framework for Improving PC Design using B...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Rajendran, T. Vignesh, O. Priyadharsini, V. Jagadeesh, K. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Easwari Engineering College Department of Electronics and Communication Engineering Chennai India Akshaya College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India GRT Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350397253

Cyberspace is massively expanding every day, and the users of these digital devices are looking for more innovative applications to ease their day-to-day work. The main objective of any device is to use available system resources at a maximum optimum level and give high performance to the users. Especially in personal computers (PC), there is still a massive gap in the user experience and utilization of available system resources. There are several situations where error conditions and system responsiveness lag. Over several years the digital world evolved and provided high-performance computing. Furthermore, the PC design enhancements could be carryout through the participation of PC users. That could be accomplished by collecting the system performance indicators, system behavior and properties, software application usage, user working log, and other wide range of real-time data from the PC. These telemetry data needed to be retrieved from an extensive array of PCs then these datasets would be reposited on the centralized servers. By applying the big data analytical methods and other mining algorithms over these extensive datasets to identify the hidden patterns and discover more knowledge about the key indicators that would help improve PC performance and user experiences. Through this knowledge acquisition model, the organizations and research institutions could develop the most resilient and adoptable PC designs and software applications developments. © 2023 IEEE.

关键词： Personal computers

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MedLogic-AQA: Enhancing Medical Question Answering with Abstractive Models Focusing on Logical Structures

MedLogic-AQA: Enhancing Medical Question Answering with Abst...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zafar, Aizan Mishra, Kshitij Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761681

In Medical question-answering (QA) tasks, the need for effective systems is pivotal in delivering accurate responses to intricate medical queries. However, existing approaches often struggle to grasp the intricate logical structures and relationships inherent in medical contexts, thus limiting their capacity to furnish precise and nuanced answers. In this work, we address this gap by proposing a novel Abstractive QA system MEDLOGIC-AQA that harnesses First Order Logic (FOL) based rules extracted from both context and questions to generate well-grounded answers. Through initial experimentation, we identified six pertinent first-order logical rules, which were then used to train a Logic-Understanding (LU) model capable of generating logical triples for a given context, question, and answer. These logic triples are then integrated into the training of MEDLOGIC-AQA, enabling effective and coherent reasoning during answer generation. This distinctive fusion of logical reasoning with abstractive QA equips our system to produce answers that are logically sound, relevant, and engaging. Evaluation with respect to both automated and human-based demonstrates the robustness of MEDLOGIC-AQA against strong baselines. Through empirical assessments and case studies, we validate the efficacy of MEDLOGIC-AQA in elevating the quality and comprehensiveness of answers in terms of reasoning as well as informativeness. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Roselinkiruba, R. Jude Moses Anto Devakanth, J. Sharmila, L. Vasumathy, M. Misba, M. Saranya Jothi, C. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai Avadi India Madanapalle Institute of Technology and Science Department of Computer Applications Andra Pradesh India Department of Information Technology Kingston Engineering College Vellore India

ISBN: (纸本)9798331515720

Steganography is the method that convert data into bits to make it suitable for hiding. While embedding data the factors such as stego image quality and distortion which cannot be observed by the human eyes. The aim of this study is to increase the stego image with good quality. Initially, the image pixels are separated into different bit planes then it is compared with the secret data bit planes using Fruitfly Optimization Improved Hybridized Algorithm Pixel Similarity (FOIS-PS). For that similar pixel Fitness Function (FF) is applied based on the three important criteria such as edge, entropy and correlation. Finally, the data is embedded in the LSB bits based on their Embedding Capacity (EC). Using this method, the size of the input is reduced. Then, the data is embedded based on difference of pixels within each group of similar pixels. Evaluation metrics shows the quality of the stego image is boosted and also reduces the processing time. © 2024 IEEE.

关键词： Image quality

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Multi-Plant Leaf Disease Detection and Classification using Convolution Neural Network Aad Image Processing

Multi-Plant Leaf Disease Detection and Classification using ...

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2024 IEEE International Conference on Recent Advances in Science and engineering technology, ICRASET 2024

作者： Aishwarya, S.S. Ravikumar, Pushpa Chaithra, I.V. Sagar, Deeksha AIT Adichunchanagiri Institute of Technology Karnataka Chikmagalur India AIT Adichunchanagiri Institute of Technology Department of Computer Science and Engineering Karnataka Chikmagalur India

ISBN: (纸本)9798350388602

Every nation's basic need is the agricultural products. Disease-ridden leaves have an effect on the nation's agricultural output and financial resources. This research provides a deep learning based plant leaf disease classification and detection method. The website (Plant Village dataset) provided the photos that were used. Since tomatoes, peppers, and potatoes are the most prevalent plant species worldwide and in Iraq specifically, we have used them as examples of certain plant species in our study. There are 20636 photos of plants and their illnesses in this data set. Plant leaf diseases are sorted into 15 class labels in the proposed system using a convolutional neural network (CNN). Of these classes, 12 are dedicated to diseases. Several various plants that were found, including three groups for healthy leaves and various microorganisms including fungus. Consequently, we achieved exceptional precision in both training and testing;specifically, we achieved 98.29% accuracy in training and 98.029% accuracy in testing across all utilized data sets. Pattern recognition is necessary in order to diagnose these disorders. Using remote sensing techniques to investigate multi- and hyper-spectral picture captures is a frequent approach in this situation. Techniques that use this strategy frequently use digital image processing capabilities to accomplish their objectives. There are numerous kinds of pattern recognition algorithms that provide accurate disease detection. Plant diseases are currently detected using principal component analysis and back propagation. These algorithms are acquired by neural network training supervision. © 2024 IEEE.

关键词： Convolutional neural networks

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An Efficient Solution for the Collusion Problem of DECO 17

An Efficient Solution for the Collusion Problem of DECO

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17th International Conference on Information Security and Cryptology, ISCTurkiye 2024

作者： Sen, Ugur Osmanoglu, Murat Selcuk, Ali Aydin Institute of Applied Mathematics Middle East Technical University Ankara Turkey Department of Computer Engineering Ankara University Ankara Turkey Department of Computer Engineering TOBB University of Economics and Technology Ankara Turkey

ISBN: (纸本)9798331533649

DECO is one of the state-of-The-Art TLS evidence protocols that enables privacy-preserving verification without necessitating server-side modifications. It allows applications to extract verifiable data from websites. However, DECO is vulnerable to collusion attacks, where the prover and verifier may secretly conspire, even if DECO itself remains secure. This vulnerability limits DECO's ability to transfer data to smart contracts. To address the collusion issue, decentralized oracle networks have been integrated with DECO, involving multiple executions for the same attestation and relying on majority consensus to determine the correct outcome. While this approach mitigates collusion risk, it is inefficient. In this paper, we address the collusion vulnerability in DECO by proposing an enhanced approach that expands the verifier's role to a set of n verifiers within a single DECO execution, utilizing a Distributed Verifiable Random Function (DVRF). This method requires the prover to obtain consensus from all n verifiers, thereby increasing the difficulty of collusion and enhancing the security of the system. Furthermore, the proposed solution is more efficient than executing n separate DECO protocols and provides verifiable features that facilitate integration with slashing and reward mechanisms, thereby improving its applicability in decentralized systems. © 2024 IEEE.

关键词： Smart contract

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Survey on Smartphone Sensors and User Intent in Smartphone Usage 8

Survey on Smartphone Sensors and User Intent in Smartphone U...

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8th IEEE International Conference for Convergence in technology, I2CT 2023

作者： Bhatele, Priyanka Bedekar, Mangesh Vishwakarma Institute of Technology Department of Computer Engineering Maharastra Pune India MIT World Peace University School of Computer Engineering and Technology Maharashtra Pune India

ISBN: (纸本)9798350334012

Smartphone/Tablet users are approximately 3 million all over the world. It is likely to increase by several 100 million in the next few years. Around 40% of these users read online. Explicit means of feedback system is strongly based. It provides the most accuracy when rating an online learning application. Increase in the availability of content over the web and high user engagements, has led to the demand of the means that implicitly provide feedback. Implicit feedback relies on understanding the quality of the content based on the user activities performed over the web applications. Less accuracy is the limitation. It needs to stand with a support to provide as strong base as the explicit model does. Clipboard copy operations on the webpage provide an implicit insight to the user intentions. Screen activities like scrolling and pinch to zoom further can statistically be proven the positive indicators of user interest. Smartphone sensors like Gyroscope and Accelerometer silently sense human screen activities and mobile gestures. This review paper is based on the understanding of smartphone sensors and the inferences of user intent through it. The dig is based on various implicit indicators like mobile gestures, smartphone sensors and clipboard copy operations. © 2023 IEEE.

关键词： Gyroscopes

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An Efficient Dilated DTCN with Residual LSTM-based Allergenicity Prediction for Chemical Compounds using Optimal Weighted Feature Selection and Improved Optimization Mechanism 2

An Efficient Dilated DTCN with Residual LSTM-based Allergeni...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Padmavathy, T. Thenmozhi, M. Madhavan, Thirumurthy SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India SRM Institute of Science and Technology Department of Genetic Engineering Chennai India

ISBN: (纸本)9798350365269

Allergy is the sudden response of the immunity system which arises later the disclosure of allergens like chemicals, proteins and peptides. In the early times, multiple techniques were formed for allergenicity forecasting of peptides and proteins. However, there is no technique to anticipate the chemical and potential allergenic. The standard food allergen recognition mainly depends on vitro and vivo research, which frequently needs more time and is expensive. Artificial Intelligence (AI) operates a quick food allergen detection model, which solves the above-defined problems and becomes an effective auxiliary tool. Evaluation of potential allergenicity of protein is required at any movement of transgenic proteins implemented in the food chain. Therefore, it is necessary to solve more complications involved in the conventional allergenicity prediction technique for chemical compounds. Thus, a new allergenicity prediction framework for chemical compounds is designed using deep learning techniques. Initially, essential allergenicity data are accumulated from the standard measures. Next, the acquired data are presented for the optimal weighted feature selection stage. In this phase, the weights and features are selected optimally using Opposition Tasmanian Devil Optimization (OTDO). Further, the adopted optimal weighted features are offered to the allergenicity forecasting stage. Here, the allergenicity is predicated on using the developed "Dilated Deep Temporal Context Networks with Residual Long Short-Term Memory Networks (DiDNet-ResLSTM)". Hence, the recommended allergenicity prediction model effectively higher performance rate in different experimental observations. © 2024 IEEE.

关键词： Forecasting

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A System for Language Translation using Sequence-to-sequence Learning based Encoder 5

A System for Language Translation using Sequence-to-sequence...

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5th International Conference on Emerging Smart Computing and informatics, ESCI 2023

作者： Sarode, Sonia Thatte, Raghav Toshniwal, Kajal Warade, Jatin Bidwe, Ranjeet Vasant Zope, Bhushan SCTR's Pune Institute of Computer Technology Department of Computer Engineering Pune India Lavale Maharashtra Pune India

ISBN: (纸本)9781665475242

Hindi is the mother tongue of nearly 133 crore Indians. Along with India, it is spoken in Nepal, Fiji, and Bangladesh. Since good knowledge of English is not common, there is a good opportunity for machine translation from English to Hindi and vice versa. Language translation is one task in which machines lag behind human power [1]. One task where machines fall short of human ability is language translation. Rule-Based Translation (RBT) systems and Statistical Machine Translation (SMT) systems are the conventional systems used for language translation. Rule Based Translation requires in-depth knowledge of the language. RBT is a fairly complicated system that can and must include many rules in order to improve quality. SMT is one of the traditional approaches to the machine translation issue. This technique works well with pairs of languages with comparable grammatical structures and requires enormous data sets. This paper proposes a better approach - a neural network model that uses 'Recurrent Neural Network' (RNN) and 'Gated Recurrent Unit' (GRU). The system consists of an RNN-encoder and RNN-decoder architecture and an attention mechanism to deal with long sentences. © 2023 IEEE.

关键词： Recurrent neural networks

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