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Optimization of Interval Type-2 Fuzzy Logic System Using Grasshopper Optimization Algorithm

computers, Materials & Continua 2022年第5期71卷 3513-3531页

作者： Saima Hassan Mojtaba Ahmadieh Khanesar Nazar Kalaf Hussein Samir Brahim Belhaouari Usman Amjad Wali Khan Mashwani Institute of Computing Kohat University of Science and TechnologyKohatPakistan Department of Mechanical Materials and Manufacturing EngineeringFaculty of EngineeringUniversity of NottinghamUK College of Computer Sciences and Mathematics Tikrit UniversityIraq Division of Information and Computing Technology College of Science and EngineeringHamad Bin Khalifa UniversityQatar Department of Computer Science Sir Syed University of Engineering and TechnologyPakistan Institute of Numerical Sciences Kohat University of Science and TechnologyKohatPakistan

The estimation of the fuzzy membership function parameters for interval type 2 fuzzy logic system(IT2-FLS)is a challenging task in the presence of uncertainty and *** optimization algorithm(GOA)is a fresh population based meta-heuristic algorithm that mimics the swarming behavior of grasshoppers in nature,which has good convergence ability towards *** main objective of this paper is to apply GOA to estimate the optimal parameters of the Gaussian membership function in an *** antecedent part parameters(Gaussian membership function parameters)are encoded as a population of artificial swarm of grasshoppers and optimized using its *** of the consequent part parameters are accomplished using extreme learning *** optimized IT2-FLS(GOAIT2FELM)obtained the optimal premise parameters based on tuned consequent part parameters and is then applied on the Australian national electricity market data for the forecasting of electricity loads and *** forecasting performance of the proposed model is compared with other population-based optimized IT2-FLS including genetic algorithm and artificial bee colony optimization *** of the performance,on the same data-sets,reveals that the proposed GOAIT2FELM could be a better approach for improving the accuracy of the IT2-FLS as compared to other variants of the optimized IT2-FLS.

关键词： Parameter optimization grasshopper optimization algorithm interval type-2 fuzzy logic system extreme learning machine electricity market forecasting

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Collaborative Filtering Recommendation Algorithm Combining Tag Relevance and Score Differences 8

Collaborative Filtering Recommendation Algorithm Combining T...

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8th International Conference on Intelligent Informatics and Biomedical sciences, ICIIBMS 2023

作者： Shi, Guangyang Yu, Wanjun Chen, Xiongmei Li, Tianning Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China College of Sciences Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9798350369564

The aim of this study is to propose a collaborative filtering recommendation algorithm that addresses the issue of inaccurate user preference mining in traditional collaborative filtering algorithms. The algorithm combines tag correlation and score differences to achieve accurate recommendations. It incorporates Newton's law of cooling to model the attenuation of users' interest in tags over time, and calculates user interest based on tag correlations. Additionally, it considers the maximum difference in user ratings from historical records, taking into account the impact of these factors on item ratings. The algorithm also considers the rating time to capture recent user preferences. Finally, it performs a weighted fusion of user label interest similarity and rating difference similarity to identify the most similar neighbors and make item predictions, scores, and recommendations. Experimental results demonstrate that this algorithm significantly improves recommendation accuracy. © 2023 IEEE.

关键词： Collaborative filtering

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A Study on Gait Pattern Identification and MNA Detection Methodologies used for Human Medical Diagnosis using Wearable Sensors 7

A Study on Gait Pattern Identification and MNA Detection Met...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Pushpalatha, S. Math, Shrishail Puiari, Chandrakanth G Dr. Ambedkar Institute of Technology Dept. of Information Science and Engineering Bengaluru India Sri Krishna Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350397284

In ambulatory monitoring, different sensors such as pulse oximetry, accelerometer, gyroscope, etc. are used for monitoring probes. Generally, these sensors are used for monitoring daily physical activity, stress, and sleep pattern of the respective probe. However, the measurement obtained from these sensors is highly affected due to motion and noise artifacts (MNAs). This study conducts a survey of various existing methodologies presented for mitigating motion and noise artifact of measurement obtained through wearable sensors. The survey is composed of both supervised and non-supervised techniques used for mitigating MNAs, detecting HAR, and classifying Obstructive Sleep Apnea (OSA) using wearables. This survey further highlights research issues in monitoring probes using wearable sensors and presents a future solution to overcome research issues. © 2023 IEEE.

关键词： Wearable sensors

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LSTM Based Neural Network Model for Anomaly Event Detection in Care-Independent Smart Homes

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computer Modeling in engineering & sciences 2024年第9期140卷 2689-2706页

作者： Brij B.Gupta Akshat Gaurav Razaz Waheeb Attar Varsha Arya Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413TaiwanChina Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Computer Engineering Ronin InstituteMontclairNJ 07043USA Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Business Administration Asia UniversityTaichung413TaiwanChina Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia School of Science and Technology Hong Kong Metropolitan University(HKMU)Hong KongChina

This study introduces a long-short-term memory(LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes,focusing on the critical application of elderly fall *** balances the dataset using the Synthetic Minority Over-sampling Technique(SMOTE),effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification *** proposed LSTM model is trained on the enriched dataset,capturing the temporal dependencies essential for anomaly *** model demonstrated a significant improvement in anomaly detection,with an accuracy of 84%.The results,detailed in the comprehensive classification and confusion matrices,showed the model’s proficiency in distinguishing between normal activities and *** study contributes to the advancement of smart home safety,presenting a robust framework for real-time anomaly monitoring.

关键词： LSTM neural networks anomaly detection smart home health-care elderly fall prevention

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Multinomial Naive Bayes Based Machine Learning Analysis of Twitter Sentiment 2

Multinomial Naive Bayes Based Machine Learning Analysis of T...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Saravanan, U.K.Balaji Vijay, M. Shreedhar, T. Rajasekar, G. Yashwanth, R. Shakthipriya, P. Sona College of Technology Information Technology Salem India Vmkv Enginerring College Dept. of Computer Science and Engineering Salem India

ISBN: (纸本)9798350347579

Sentiment analysis identifies and categorizes thoughts and feelings expressed in the source text. Social media uses tweets, status updates, and blog columns to generate sensitive data. SA on user-generated data helps understand crowd opinion. Due to slang and spelling errors, Twitter sentiment analysis (TSA) is much more complex than general sentiment analysis. Every day, millions of tweets are generated on various topics. Machine learning algorithms can be used to solve this problem-the proposed machine learning algorithm includes preprocessing, weight calculation, feature extraction, and classification. First, we collected a Kaggle dataset from Twitter sentiment analysis. In the second step, the data preprocessing steps mainly include stop words, emoji, and repeated characters in words and URLs due to their low importance. Then, we used tokenization, normalization, stemming, and lemmatization to achieve better results. In the third step, we computed weights using word2vec to represent the tweets. In the fourth step, we use a feature extraction method consisting of TF-IDF and word embedding to check based on word and noun counts. Finally, TSA can be used to explore sentiment using Multinomial Naïve Bayes (MNB) and Maximum Entropy classification. The proposed machine learning algorithm achieves better results with TSA in terms of recognition accuracy, recall, precision and F-measure. © 2023 IEEE.

关键词： Sentiment analysis

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An Adaptive Density Spatial Clustering Algorithm Based on Tuna Swarm Optimization for Image Segmentation

An Adaptive Density Spatial Clustering Algorithm Based on Tu...

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2022 International Conference on Electronic information engineering and computer science, EIECS 2022

作者： Zhou, Wei Wang, Limin Han, Xuming Wang, Yizhang School of Computer Science and Technology Changchun University of Science and Technology Changchun China School of Information Guangdong University of Finance & Economics Guangzhou China College of Information Science and Technology Jinan University Guangzhou China College of Information Engineering Yangzhou University Yangzhou China

ISBN: (纸本)9781510663312

The density-based spatial clustering of applications with noise (DBSCAN) method is exceptionally sensitive to the selection of parameters, making it difficult to obtain more accurate clustering results. To solve the above problem, we propose an adaptive TSO-DBSCAN algorithm using the excellent global search performance of the Tuna Swarm Optimization (TSO) method. We take the clustering radius parameter as the search objective of the TSO method. Then, the obtained evaluation metrics are used as the fitness function of the TSO method to adaptively search for the best parameter value and the optimal clustering result. The experimental results show that the proposed method has better clustering accuracy and effect on the processing of image segmentation. © 2023 SPIE.

关键词： Image segmentation

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Unsupervised Domain Adaptation Methods for Causal Correlation

Unsupervised Domain Adaptation Methods for Causal Correlatio...

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2023 International Annual Conference on Complex Systems and Intelligent science, CSIS-IAC 2023

作者： Zhou, Liyuan Dou, Quansheng Tang, Huanling Li, Xiujuan Liu, Yanchao College of Information and Electronic Engineering Shandong Technology and Business University Yantai China School of Computer Science and Technology Shandong Technology and Business University Yantai China School of Computer Science and Technology Kashi University Xinjiang China

ISBN: (纸本)9798350309003

Unsupervised domain adaptation methods enhance the ability of models to generalize by minimizing the distributional disparities between the source and target domains through the transfer of knowledge from different domains and tasks, utilizing both labeled and unlabeled data. However, a challenge arises due to the presence of pseudo-correlations between certain 'confounders' and category labels, leading to a decrease in model prediction accuracy. To tackle this issue, this paper introduces the Causal Correlation Reweighting Module (CCRM). The CCRM samples features related to the labels and features independent of the labels, evaluates their independence, applies reweighting to the label-related features, and reduces the pseudo-correlation between the label-independent features and category labels. This approach results in improved model generalization performance and enhances the model's ability to handle unsupervised domain adaptation tasks. The proposed method is validated on widely-used domain adaptive datasets such as Digits, Office-Home and VisDA-2017, demonstrating higher accuracy compared to previous domain adaptation models. © 2023 IEEE.

关键词： Knowledge management

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The Industrial Internet Security Situation Assessment Method Based on AHP-CRITIC Hybrid Weighting 2

The Industrial Internet Security Situation Assessment Method...

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2nd International Conference on Signal Processing and Intelligent Computing, SPIC 2024

作者： Yang, Ling Xu, Shuheng Hou, Lixin Tao, Yaodong Shenyang LiGong University School of Information Science and Engineering Shenyang China Beijing DualPi Intelligent Security Technology Shenyang China Jilin Agricultural University College of Information and Technology Changchun China Beijing Jiaotong University School of Computer and Information Technology Beijing China

ISBN: (纸本)9798350368888

The assessment index system for industrial Internet network security relies too heavily on expert knowledge and overlooks the impact of objective factors on situation assessment. This paper addresses this issue by focusing on the characteristics of industrial networks, specifically the impact of asset vulnerabilities and alarm events on network situation. A comprehensive weighted hierarchical industrial Internet situation assessment method is proposed. The final comprehensive weight is obtained using the least square method, combined with the supervisor weight calculated by the analytic hierarchy process and the objective weight calculated by the CRITIC method. Finally, a network security experiment center is used to carry out situation assessment, and the evaluation value is calculated. Experimental results demonstrate that this method provides a more objective and accurate approach to situation assessment. © 2024 IEEE.

关键词： Analytic hierarchy process

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Network bandwidth variation-adapted state transfer for geo-replicated state machines and its application to dynamic replica replacement

Network bandwidth variation-adapted state transfer for geo-r...

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作者： Chiba, Tairi Ohmura, Ren Nakamura, Junya Department of Computer Science and Engineering Toyohashi University of Technology Aichi Japan Information and Media Center Toyohashi University of Technology Aichi Japan

This paper proposes a new state transfer method for geographic state machine replication (SMR) that dynamically allocates the state to be transferred among replicas according to changes in communication bandwidths. SMR improves fault tolerance by replicating a service to multiple replicas. When a replica is newly added or recovered from a failure, the other replicas transfer the current state of the service to it. However, in geographic SMR, the communication bandwidths of replicas are different and constantly changing. Therefore, existing state transfer methods cannot fully utilize the available bandwidth, and their state transfer time increases. To overcome this problem, our method divides the state into multiple chunks and assigns them to replicas based on each replica's bandwidth so that the broader a replica's bandwidth is, the more chunks it transfers. The proposed method also updates the chunk assignment of each replica dynamically based on the currently estimated bandwidth. The performance evaluation on Amazon EC2 shows that the proposed method reduces the state transfer time by up to 47% compared to the existing one. In addition, we apply the proposed method to dynamic replacement of replicas, which can mitigate latency degradation caused by network trouble, and evaluate how fast the method can relocate a replica. © 2022 John Wiley & Sons, Ltd.

关键词： Bandwidth

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Intrusion Detection in the Internet of Things Using Fusion of GRU-LSTM Deep Learning Model

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Intelligent Automation & Soft Computing 2023年第8期37卷 2279-2290页

作者： Mohammad S.Al-kahtani Zahid Mehmood Tariq Sadad Islam Zada Gauhar Ali Mohammed ElAffendi Department of Computer Engineering Prince Sattam Bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer Engineering University of Engineering and TechnologyTaxila47050Pakistan The FAMLIR Group The University of LahoreLahore54000Pakistan Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan Faculty of Computing International Islamic UniversityIslamabad44000Pakistan EIAS Data Science and Blockchain Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

Cybersecurity threats are increasing rapidly as hackers use advanced *** a result,cybersecurity has now a significant factor in protecting organizational *** detection systems(IDSs)are used in networks to flag serious issues during network management,including identifying malicious traffic,which is a *** remains an open contest over how to learn features in IDS since current approaches use deep learning *** learning,which combines swarm intelligence and evolution,is gaining attention for further improvement against cyber *** this study,we employed a PSO-GA(fusion of particle swarm optimization(PSO)and genetic algorithm(GA))for feature selection on the CICIDS-2017 *** achieve better accuracy,we proposed a hybrid model called LSTM-GRU of deep learning that fused the GRU(gated recurrent unit)and LSTM(long short-term memory).The results show considerable improvement,detecting several network attacks with 98.86%accuracy.A comparative study with other current methods confirms the efficacy of our proposed IDS scheme.

关键词： Cyber security deep learning intrusion detection PSO-GA CICIDS-2017 intelligent system security and privacy IoT

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