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Optimized deep belief network and unsupervised deep learning methods for disease prediction

Journal of Intelligent and Fuzzy Systems 2023年第6期45卷 9571-9589页

作者： Shenbagavalli, S.T. Shanthi, D. Department of Computer Science and Engineering PSNA College of Engineering and Technology Tamilnadu Dindigul India

Due to the vast amount of patient health data, automated healthcare systems still struggle to classify and diagnose various ailments. Learning redundant data also reduces categorization accuracy. A Deep Belief Network (DBN) has been used to precisely extract the most important aspects from clinical data by ignoring irrelevant/redundant features. Due of many learning variables, training is complicated. Similarly, the hybrid model has been employed by ensemble Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) to categorize diseases. But, its efficiency depends on the proper choice of kernels and hyper-parameters. Therefore, this paper develops an efficient feature extraction and classification model for healthcare systems. First, several medical data related to the patient’s health are collected. Then, an Optimized DBN (ODBN) model is presented for maximizing the accurateness of DBN by optimizing the learning variables depends on the Ant Lion Optimization (ALO) algorithm. With learning ODBN, the most relevant features are extracted with reduced computational complexity. After that, the CNN-LSTM with Unsupervised Fine-tuned Deep Self-Organizing Map (UFDSOM)-based classifier model is designed to categorize the extracted features into categories of illnesses. In this novel classifier, dropout normalization and parameter tuning processes are applied to avoid overfitting and optimize the hyper-parameters, which results in a less training period. In the end, studies utilizing publically accessible datasets show that the ODBN with CNN-LSTM-UFDSOM system outperforms classical models by 98.23%. © 2023 – IOS Press. All rights reserved.

关键词： Support vector machines

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An Effective Analysis and Exploration of Cutting-Edge Machine Learning for Protein Structure and Sequence Prediction

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SN computer science 2024年第6期5卷 1-12页

作者： Alam, Afaque Kumar, Mukesh Department of Computer Science and Engineering National Institute of Technology Patna

When processing large amounts of data, proteins play a key role in biological processes. Protein structure prediction relies on a procedure called relevant feature selection. To accomplish classification, a feature selection technique must first identify the most important features inside the massive dataset. The proposed methods improve protein structure prediction by providing data from the protein identification and sequencing process. Here we present a novel method for predicting protein structure using PSI sequencing. Gibbs Entropy Simulated Forging is used to pick important properties from a protein dataset. Gibbs Entropy is used to quickly locate the important amino acid properties from the massive dataset. The big data protein is sequenced using the Edman Firefly Optimization procedure after the appropriate features have been identified. A firefly's light intensity is calculated with the use of an objective function. Selected amino acid characteristics are ordered by light intensity rating. As a result, it is put to use in the fast and precise formation of protein sequences. This results in improved accuracy when sequencing proteins and a shorter sequencing time when determining each peptide's amino acid sequence. Accuracy and recall in the GESF algorithm procedure have increased while protein sequencing has required less time. The protein sequence is then constructed by applying Edman Firefly Optimization to a subset of characteristics. To improve protein correctness with little efforts investment, peptide residues are sequenced using EFO. Several large protein datasets are used to conduct an experimental evaluation in terms of precision, recall, false positive rate (FPR), sequencing accuracy (SQR), and sequencing time. The findings demonstrate that GESF-EFO improves upon existing methods of protein sequencing in the context of massive datasets. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Accuracy EFO GESF SA ST

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Innovative Rumor Detection on Social Media Text: A Comprehensive Study of Dual Co-Attention Ensemble Based Voting Approach 5th

Innovative Rumor Detection on Social Media Text: A Comprehen...

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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Gangone, Anjali Bharat Kumar, G.J. Swapna, Mandu Department of Computer Science and Engineering CMR Institute of Technology Hyderabad India Department of Computer Science and Engineering Sreyas Institute of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9789819788606

This study delves into the crucial task of rumor detection amidst the rapid spread of information online, focusing on the efficacy of advanced dual co-attention ensemble models for ensuring digital communication’s reliability. By integrating Gated Recurrent Unit (GRU) Representation, Self-Attention (SA) Models, and Bidirectional Encoder Representation from Transformers (BERT) Sentiment Models, alongside various Natural Language Processing (NLP) techniques, the research examines the potential of these models to improve rumor detection accuracy significantly. The investigation encompasses eight distinct models: the GRU Representation Model, the SA Model, the BERT Sentiment Model, and various dual co-attention ensemble combinations thereof (GRU + SA, SA + BERT, BERT + GRU, and GRU + SA + BERT), employing a sophisticated voting mechanism for rumor classification. This comprehensive evaluation across key performance metrics Accuracy, Precision, Recall, and F1 Score revealed the substantial impact of leveraging dual co-attention ensemble model strengths, particularly through dual co-attention and a strategic voting mechanism. Among the dual co-attention ensemble models, the SA + GRU + BERT configuration emerged as a superior performer, achieving remarkable results with an accuracy of 93.5%, a precision of 92%, a perfect recall of 99%, and an F1 score of 95%. These findings underscore the dual co-attention ensemble models’ exceptional capability to address rumor detection challenges effectively. The study not only showcases the promising prospects of utilizing cutting-edge AI for rumor detection but also provides insights for refining these models further. It suggests avenues for future research, including model finetuning, ethical considerations, and the necessity for real-time monitoring. This research underscores the transformative potential of AI in enhancing the accuracy of online information, paving the way for future advancements in rumor detection. © The Author(s),

关键词： Natural language processing systems

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Intelligent Vehicular Communication Using Vulnerability Scoring Based Routing Protocol

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Intelligent Automation & Soft Computing 2023年第1期35卷 31-45页

作者： M.Ramya Devi I.Jasmine Selvakumari Jeya Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641050India

Internet of Vehicles(IoV)is an intelligent vehicular technology that allows vehicles to communicate with each other via *** and the Internet of Things(IoT)enable cutting-edge technologies including such self-driving *** the existing systems,there is a maximum communication delay while transmitting the *** proposed system uses hybrid Cooperative,Vehicular Communication Management Framework called CAMINO(CA).Further it uses,energy efficient fast message routing protocol with Common Vulnerability Scoring System(CVSS)methodology for improving the communication delay,*** improves security while transmitting the messages through *** this research,we present a unique intelligent vehicular infrastructure communication management *** framework includes additional stability for both short and long-range mobile *** also includes built-in cooperative intelligent transport system(C-ITS)capabilities for experimental verification in real-world *** addition,an energy efficient-fast message distribution routing protocol(EE-FMDRP)has been *** combines the benefits between both temporal and direction oriented routing *** has been suggested for distributing information from the origin ends to the predetermined objective in a quick,accurate,and effective manner in the event of an *** critical value scale score(CVSS)employ ratings to measure the assault probability in Markov *** of chained transitions allow us to statistically evaluate the integrity of a group of *** the proposed method helps to enhance the vehicular *** CAMINO with energy efficient fast protocol using CVSS(CA-EEFP-CVSS)method outperforms in terms of shortest transmission latency achieves 2.6 sec,highest throughput 11.6%,and lowest energy usage 17%and PDR 95.78%.

关键词： Intelligent automation intelligent transport system vehicular networks markov chains internet of vehicles critical value scale score

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Smart automated highway lighting system using IoT: a survey

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Energy Informatics 2024年第1期7卷 1-28页

作者： Achar, Tejaswini Eshwar Rekha, C. Shreyas, J. Department of Computer Science and Engineering Government Engineering College Koppal Talakal India Department of Information Science and Engineering Bapuji Institute of Engineering and Technology Davanagere India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

Efficient highway lighting is crucial for ensuring road safety and reducing energy consumption and costs. Traditional highway lighting systems rely on timers or simple photosensors, leading to inefficient operation by illuminating lights when not needed or failing to adjust to changing conditions. The emergence of the Internet of Things (IoT) and related technologies has enabled the development of smart automated highway lighting systems that can dynamically control illumination levels based on real-time data. This paper provides a comprehensive review of the current state-of-the-art in smart automated highway lighting systems employing IoT technologies. Key components, communication protocols, data processing techniques, and lighting control strategies are discussed. The integration of renewable energy sources and energy storage systems is explored for environmentally sustainable operations. Practical implementation case studies are analyzed to highlight benefits and challenges. Open research issues and future directions for further enhancements are identified. © The Author(s) 2024.

关键词： Street lighting

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Optimal trained ensemble of classification model for satellite image classification

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Multimedia Tools and Applications 2025年第7期84卷 3745-3766页

作者： Singh, Sarabjeet Kundra, Harish Kundra, Sheetal Pratima, P. Venkata Devi, M. V. Anjana Kumar, Sushil Hassan, Mariya Department of Computer Science and Engineering Chandigarh College of Engineering and Technology Chandigarh160019 India Department of Computer Science and Engineering Guru Nanak Institutions Technical Campus Ranga Reddy District Telangana Ibrahimpatnam501506 India Department of Artificial Intelligence and Data Science Guru Nanak Institute of Technology Ranga Reddy District Telangana Ibrahimpatnam501506 India Department of Computer Science and Engineering Central University of Haryana Jant-Pali Mahendergarh123031 India

Satellite image classification is the most significant remote sensing method for computerized analysis and pattern detection of satellite data. This method relies on the image's diversity structures and necessitates a thorough validation of the training data using the chosen classification algorithm. The classifying of remotely sensed images refers to the process of classifying pixels into a fixed set of distinct classes based on their data values. The pixel is categorized into that class if it complies with a specific set of requirements. A new SIC scheme is suggested in this work, where, Contrast limited adaptive histogram equalization (CLAHE) is deployed for pre-processing the image. Further, statistical features, Vegetation Indices (VI) and Improved Hybrid Vegetation Index (IHVI) are extracted from the preprocessed image. Then, we deploy 2 phase classification process for classifying the image. In the first phase, Bi-Directional Gated Recurrent Unit (BI-GRU), Convolutional Neural Network (CNN) and Deep Max Out (DMO) are used. In 2nd phase, optimized RNN is used for determining the final classification result. Also, the training of the model is done by the new Chaotic Opposition Behavior Improvised Coot Algorithm (COBI-CA) optimization via tuning the optimal weights. The COOT method has been enhanced and is now the suggested algorithm. Lastly, we conduct an analysis to compare the suggested Satellite Image Classification (SIC) model's performance to the traditional approaches in terms of statistical analysis, precision, accuracy, and error measure. In comparison to other conventional methods, the accuracy of the suggested model is 97%. © This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply 2024.

关键词： Pixels

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Adversarial attacks and defenses for digital communication signals identification

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Digital Communications and Networks 2024年第3期10卷 756-764页

作者： Qiao Tian Sicheng Zhang Shiwen Mao Yun Lin College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China College of Information and Communication Engineering Harbin Engineering UniversityHarbin150000China Department of Electrical and Computer Engineering Auburn UniversityAuburnAL36849USA

As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management *** has become a promising solution to this problem due to its powerful modeling capability,which has become a consensus in academia and ***,because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space,the physical layer digital communication signals identification model is threatened by adversarial *** examples pose a common threat to AI models,where well-designed and slight perturbations added to input data can cause wrong ***,the security of AI models for the digital communication signals identification is the premise of its efficient and credible *** this paper,we first launch adversarial attacks on the end-to-end AI model for automatic modulation classifi-cation,and then we explain and present three defense mechanisms based on the adversarial *** we present more detailed adversarial indicators to evaluate attack and defense ***,a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model,which should be paid more attention in future research.

关键词： Digital communication signals identification AI model Adversarial attacks Adversarial defenses Adversarial indicators

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Preventing 51% Attack by Using Consecutive Block Limits in Bitcoin

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IEEE Access 2024年 12卷 77852-77869页

作者： Mahmood Babur, Sohail Ur Rehman Khan, Shafiq Yang, Jing Chen, Yen-Lin Soon Ku, Chin Yee Por, Lip Government Murray College Department of Information Technology Sialkot51010 Pakistan Capital University of Science and Technology Department of Computer Science Islamabad44730 Pakistan Universiti Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

In permissionless blockchain systems, Proof of Work (PoW) is utilized to address the issues of double-spending and transaction starvation. When an attacker acquires more than 50% of the hash power of the entire network, they gain the ability to engage in double-spending activities, posing a significant threat to the PoW consensus algorithm. This research focuses on the consensus algorithm employed in the Bitcoin system, explaining how it operates and the security challenges it faces. The proposed modification to the PoW algorithm imposes a restriction on miners: they are not allowed to accept consecutive blocks from the same miner into the final local blockchain to prevent the 51% attack problem. This modification supports transactions that require six confirmations. In the event an attacker attempts a 51% attack with a private chain that consists of fewer than 6 blocks, it becomes easier to detect a double-spending attack before accepting the attacker's private chain. The modified algorithm introduces a 'Safe Mode Detection Algorithm' that scrutinizes incoming blocks for adjustments at the top of the local blockchain. If inconsistencies are identified, the consensus algorithm proceeds cautiously by comparing the UTXO dictionaries from the attacker's chain with those from the miner's own blockchain. This meticulous comparison aims to detect instances of double-spending. If such instances are detected, the miner rejects the attacker's chain, establishing a double-spend-free environment and thwarting 51% attacks. © 2013 IEEE.

关键词： Blockchain

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Event Management System Using Spatial and Event Attribute Information

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SN computer science 2025年第3期6卷 1-9页

作者： Setia, Sonia Anjli, Km Bisht, Upasana Jyoti Raj, Dharm Department of Computer Science & Engineering Sharda School of Engineering & Technology Sharda University Uttar Pradesh Greater Noida India Department of Computer Science Institute of Innovation in Technology & Management Delhi Janakpuri India Department of Computer Science and Engineering ITS Engineering College Greater Noida India

The event management mechanism matches messages that have been subscribed to and events that have been published. To identify the subscriptions that correspond to the occurrence inside the category, it must first run a clustering operation on the subscription message data and determine the category to which the event belongs. In this work, we offer an on-the-fly publish-subscribe technique that can handle on-the-fly subscription changes and category modifications in a distributed environment, as well as the synchronization of spatial information and event attribute information. Additionally, without any prior information, clustering and matching processes can be carried out rapidly. The publish-subscribe method is implemented on a distributed system, and a load-balancing solution is proposed for the algorithm on the distributed system. We then constructed our cluster and used real data to empirically evaluate the suggested publish-subscribe technique. Experiments show that incorporating both similarity and relevance with frequency, compared to focusing on similarity alone, significantly decreases the number of categories which need to be matched for events. This leads to a 15.5% improvement in the speed of matching events and subscriptions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clustering algorithms Distributed systems Publish-and-subscribe systems

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Q-Learning-Assisted Meta-Heuristics for Scheduling Distributed Hybrid Flow Shop Problems

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computers, Materials & Continua 2024年第9期80卷 3573-3589页

作者： Qianyao Zhu Kaizhou Gao Wuze Huang Zhenfang Ma Adam Slowik Institute of Systems Engineering Macao University of Science and TechnologyMacao99078China Department of Electronics and Computer Science Koszalin University of TechnologyKoszalin75-453Poland

The flow shop scheduling problem is important for the manufacturing *** flow shop scheduling can bring great benefits to the ***,there are few types of research on Distributed Hybrid Flow Shop Problems(DHFSP)by learning assisted *** work addresses a DHFSP with minimizing the maximum completion time(Makespan).First,a mathematical model is developed for the concerned ***,four Q-learning-assisted meta-heuristics,e.g.,genetic algorithm(GA),artificial bee colony algorithm(ABC),particle swarm optimization(PSO),and differential evolution(DE),are *** to the nature of DHFSP,six local search operations are designed for finding high-quality solutions in local *** of randomselection,Q-learning assists meta-heuristics in choosing the appropriate local search operations during ***,based on 60 cases,comprehensive numerical experiments are conducted to assess the effectiveness of the proposed *** experimental results and discussions prove that using Q-learning to select appropriate local search operations is more effective than the random *** verify the competitiveness of the Q-learning assistedmeta-heuristics,they are compared with the improved iterated greedy algorithm(IIG),which is also for solving *** Friedman test is executed on the results by five *** is concluded that the performance of four Q-learning-assisted meta-heuristics are better than IIG,and the Q-learning-assisted PSO shows the best competitiveness.

关键词： Distributed scheduling hybrid flow shop meta-heuristics local search Q-learning

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