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Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN

Intelligent Automation & Soft Computing 2023年第3期35卷 3169-3185页

作者： M.Manikandan S.Sakthivel V.Vivekanandhan Department of Computer Science and Engineering Adhiyamaan College of EngineeringHosur635109TamilnaduIndia Department of Computer Science and Engineering Sona College of TechnologySalem636005TamilnaduIndia

Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed *** closed network is intended to share sensitive location-centric information from a source node to the base station through efﬁcient routing *** efﬁciency of the sensor node is energy bounded,acts as a concentrated area for most researchers to offer a solution for the early draining power of *** management plays a signiﬁcant role in wireless sensor networks,which was obsessed with the factors like the reliability of the network,resource management,energy-efﬁcient routing,and scalability of *** topology of the wireless sensor networks acts dri-ven factor for network efﬁciency which can be effectively maintained by perform-ing the clustering process *** solutions and clustering algorithms have been offered by various researchers,but the concern of reduced efﬁciency in the routing process and network management still *** research paper offers a hybrid algorithm composed of a memetic algorithm which is an enhanced version of a genetic algorithm integrated with the adaptive hill-climbing algorithm for performing energy-efﬁcient clustering process in the wireless sensor *** memetic algorithm employs a local searching methodology to mitigate the premature convergence,while the adaptive hill-climbing algorithm is a local search algorithm that persistently migrates towards the increased elevation to determine the peak of the mountain(i.e.,)best cluster head in the wireless sensor *** proposed hybrid algorithm is compared with the state of art clus-tering algorithm to prove that the proposed algorithm outperforms in terms of a network life-time,energy consumption,throughput,etc.

关键词： Wireless sensor networks topology clustering memetic algorithm adaptive hill climbing algorithm network management energy consumption throughput

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A novel hybrid dwarf-based Archimedes optimization (HDAO) algorithm for preserving secure data in a cloud computing environment

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Soft Computing 2024年第23期28卷 13371-13387页

作者： Alandjani, Gasim Computer Science and Engineering Department Yanbu Industrial College Yanbu Saudi Arabia

In general, cloud security is capable of providing various information, applications, services, etc. using extensive policies and progressive technologies. On the other hand, loss of data, confidentiality breaches, and loss of control are the major challenges in cloud security and they need to be solved. Therefore, this paper proposes a novel hybrid dwarf-based Archimedes optimization (HDAO) algorithm for optimal key generation and to solve both data privacy and integrity issues. The HDAO is the integration of both the dwarf optimization algorithm and Archimedes optimization algorithm. The main goal of the optimization algorithm is to solve the problem and find the optimal solutions by maximizing the objective function which makes them suitable for privacy-preserving tasks. One of the main weaknesses of the AOA algorithm is getting stuck in the local optima due to the usage of gradient-based methods. This issue is overcome using the dwarf optimization algorithm via randomization and local search strategy to complement the gradient-based approach. By employing the HDAO algorithm, the data sanitization and restoration process is effectively executed using the cloud data. In addition to this, the key extraction plays a vital role in the process of data sanitization and restoration process in which the optimization is carried out by the proposed HDAO algorithm. This paper employed five different types of datasets namely the genome, protein, general text, English text, and rand128 to evaluate the effectiveness of the proposed approach. Also, the comparative analysis is made for various parameters namely encryption time, decryption time, and avalanche effect, which is evaluated to determine the efficiency of the system. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Cloud security

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Transforming Fetal Health Monitoring: Advanced Machine Learning Innovations for Accurate Prognosis

Transforming Fetal Health Monitoring: Advanced Machine Learn...

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IEEE Punecon

作者： Nancy Jenifer A. L. Jabasheela Kavitha Subramani Vinmathi M. S Sathiya V Maheswari M Computer Science and Engineering Panimalar Engineering College Chennai

ISBN: (数字)9798331527822

ISBN: (纸本)9798331527839

Early detection of pregnancy-related illnesses, such as congenital heart defects, neural tube defects, Down syndrome, and Inborn Errors of Metabolism (IEM), is vital for ensuring the health of both the fetus and the expectant mother. Leveraging advanced ML techniques-specifically Decision Trees, KNN, and Random Forest-can significantly enhance the accuracy of predicting these conditions. Decision Trees provide a transparent, interpretable model that visualizes decision-making through a series of binary splits, enabling healthcare professionals to understand the rationale behind predictions. In contrast, KNN operates on the principle of proximity, classifying new data points based on the 'k' nearest neighbors in a multidimensional space, making it effective in scenarios with complex, non-linear relationships. Meanwhile, Random Forest improves predictive performance by aggregating the outputs of multiple Decision Trees, thus mitigating overfitting and enhancing reliability. This ensemble approach draws on the diversity of trees, each trained on random subsets of data, allowing it to capture intricate interactions among variables. Together, these ML techniques utilize extensive historical data to autonomously predict pregnancy-related conditions, forming a sophisticated decision support system that complements traditional screening methods. By integrating these advanced algorithms, healthcare providers can achieve more accurate early diagnoses, ultimately improving maternal and fetal health outcomes and paving the way for targeted interventions.

关键词： Technological innovation Accuracy Medical services Predictive models Nearest neighbor methods Decision trees Reliability Prognostics and health management Random forests Diseases

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Information Centric Networking Based Cooperative Caching Framework for 5G Communication Systems

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computers, Materials & Continua 2024年第9期80卷 3945-3966页

作者： R.Mahaveerakannan Thanarajan Tamilvizhi Sonia Jenifer Rayen Osamah Ibrahim Khalaf Habib Hamam Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Solar Energy Al-Nahrain Research Center for Renewable EnergyAl-Nahrain UniversityJadriyaBaghdad64074Iraq Faculty of Engineering Universitéde MonctonNew BrunswickE1A3E9Canada Hodmas University College Mogadishu521376Somalia Bridges for Academic Excellence Tunis1002Tunisia School of Electrical Engineering University of JohannesburgAuckland ParkJohannesburg2006South Africa

The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia *** light of the data-centric aspect of contemporary communication,the information-centric network(ICN)paradigm offers hope for a solution by emphasizing content retrieval by name instead of *** 5G networks are to meet the expected data demand surge from expanded connectivity and Internet of Things(IoT)devices,then effective caching solutions will be required tomaximize network throughput andminimize the use of ***,an ICN-based Cooperative Caching(ICN-CoC)technique has been used to select a cache by considering cache position,content attractiveness,and rate *** findings show that utilizing our suggested approach improves caching regarding the Cache Hit Ratio(CHR)of 84.3%,Average Hop Minimization Ratio(AHMR)of 89.5%,and Mean Access Latency(MAL)of 0.4 *** a framework,it suggests improved caching strategies to handle the difficulty of effectively controlling data consumption in 5G *** improvements aim to make the network run more smoothly by enhancing content delivery,decreasing latency,and relieving *** improving 5G communication systems’capacity tomanage the demands faced by modern data-centric applications,the research ultimately aids in advancement.

关键词： Information-centric networking caching schemes 5G communication non-negative matrix factorization(NMF) weighted clustering algorithm

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A Novel Music Recommendation System Using Filtering Techniques

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Informatica (Slovenia) 2024年第4期48卷 595-610页

作者： Vashishtha, Srishti Varshney, Deepika Sarin, Eva Kaur, Simran Computer Science and Engineering Dept. Bharati Vidyapeeth’s College of Engineering GGSIPU New Delhi India Computer Science and Engineering Dept. Jaypee Institute of Information Technology Noida India

With the growth of the World Wide Web, a large amount of music data is available on the Internet. A large number of people listen to music online rather than downloading and listening offline. But only some sites provide personalized and accurately recommended songs while they listen to an auto-playing playlist. Hence the need for recommendation systems arises. Two approaches can be applied to the recommendation system: content based filtering and collaborative filtering. While in content-based filtering approach, analysis on the songs’ content which has been preferred by the user in history is done and the songs with relative similarity are recommended. While latter suggests songs that certain users of similar listening pattern have preferred. But collaborative filtering-based recommendation systems not only requires time to attain stability but also might recommend unsuitable music because it is not personalized to each user’s preference. Also, the latter requires the songs to be listened by a few users already to recommend it any other user. Hence for overall analysis, answers to few questions need to be implemented in recommendation systems: the very first one is how the properties should be analyzed, next one is how the analysis should be done and last is how the songs related to the user’s preference should be chosen. So, for suggesting the better system which solves these three questions as well as provides better and personalized recommendations, In this paper we build a collaborative filtering as well as content based filtering recommendation system, where various factors determine the essence of the songs, i.e. liveliness, keys used, loudness, pitch, valence, etc., are analyzed for comparison. From the experimental analysis in has been identified that content-based filtering technique performed best on KNN machine learning classifier with accuracy of 85%. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Collaborative filtering

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Smart Home IoT Privacy and Security Preservation via Machine Learning Techniques

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computers, Materials & Continua 2023年第1期74卷 1959-1983页

作者： Mubarak Almutairi College of Computer Science and Engineering University of Hafr AlbatinSaudi Arabia

The development and use of Internet of Things(IoT)devices have grown significantly in recent *** IoT device characteristics are mainly to blame for the wide range of applications that may now be achieved with IoT *** have begun to embrace the IoT *** true and suitable devices,security faults that might be used for bad reasons,and administration of such devices are only a few of the issues that IoT,a new concept in technological progress,*** some ways,IoT device traffic differs from regular device *** with particular features can be classified into categories,irrespective of their function or ***-changing and complex environments,like a smart home,demand this classification scheme.A total of 41 IoT devices were employed in this *** build a multiclass classification model,IoT devices contributed 13 network traffic *** further preprocess the raw data received,preprocessing techniques like Normalization and Dataset Scaling were *** engineering techniques can extract features from the text data.A total of 117,423 feature vectors are contained in the dataset after stratification,which are used to further improve the classification *** this study,a variety of performance indicators were employed to show the performance of the logiboosted ***-XGB scored 80.2%accuracy following application of the logit-boosted algorithms to the dataset for classification,whereas Logi-GBC achieved 77.8%***,Logi-ABC attained 80.7%***-CBC,on the other hand,received the highest Accuracy score of 85.6%.The accuracy of Logi-LGBM and Logi-HGBC was the same at 81.37%*** suggested Logi-CBC showed the highest accuracy on the dataset when compared to existing Logit-Boosted Algorithms used in earlier studies.

关键词： IoT smart home machine learning privacy security

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Modified light GBM based classification of malicious users in cooperative cognitive radio networks

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Cyber-Physical Systems 2024年第1期10卷 104-122页

作者： Sekar, S. Jeyalakshmi, S. Ravikumar, S. Kavitha, D. Department of Information Technology SRM Valliammai Engineering College Chennai India Department of Artificial Intelligence and Data Science SRM Valliammai Engineering College Chennai India Department of Computer Science and Engineering SRM Valliammai Engineering College Chennai India

This paper proposes the Modified Light GBM to classify the Malicious Users (MUs) and legitimate Secondary Users (SUs) in the cognitive-radio network. The proposed method is to avoid the consequences of malicious users in Cooperative Spectrum Sensing (CSS) without the detection of MUs. The method is tested against the occurrence of Always No Malicious User (ANMU), always yes malicious user (AYMU), the Random Malicious User (RMU), and Opposite Malicious User (OMU) transmitting spectrum sensing data to the Fusion Center (FC) by normal secondary users. The efficiency of the proposed method is expressed via simulations and compared with other existing methods. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Signal to noise ratio

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Ghost imaging based on the control of light source bandwidth

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Chinese Physics B 2023年第3期32卷 276-279页

作者：刘兆骐白艳锋邹璇彭凡周立宇付芹傅喜泉 College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China

A scheme to improve the quality in ghost imaging(GI)by controlling the bandwidth of light source(BCGI)is *** theoretical and numerical results show that the reconstruction result with high quality can be obtained by adjusting the bandwidth range of the light source appropriately,and the selection criterion of the bandwidth is analyzed by the power distribution of the imaging target.A proof-of-principle experiment is implemented to verify the theoretical and numerical *** addition,the BCGI also presents better anti-noise performance when compared with some popular GI methods.

关键词： ghost imaging light source property spatial frequency domain

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Mango Pest Detection Using Entropy-ELM with Whale Optimization Algorithm

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Intelligent Automation & Soft Computing 2023年第3期35卷 3447-3458页

作者： U.Muthaiah S.Chitra Department of Computer Science and Engineering Sri Shanmugha College of Engineering and TechnologySankari637304India Department of Computer Science and Engineering Er.Perumal Manimekalai College of EngineeringHosur635117India

Image processing,agricultural production,andﬁeld monitoring are essential studies in the researchﬁ*** diseases have an impact on agricultural production and *** disease detection at a preliminary phase reduces economic losses and improves the quality of *** identifying the agricultural pests is usually evident in plants;also,it takes more time and is an expensive technique.A drone system has been developed to gather photographs over enormous regions such as farm areas and *** atmosphere generates vast amounts of data as it is monitored closely;the evaluation of this big data would increase the production of agricultural *** paper aims to identify pests in mango trees such as hoppers,mealybugs,inﬂorescence midges,fruitﬂies,and stem *** of the massive volumes of large-scale high-dimensional big data collected,it is necessary to reduce the dimensionality of the input for classify-ing *** community-based cumulative algorithm was used to classify the pests in the existing *** proposed method uses the Entropy-ELM method with Whale Optimization to improve the classiﬁcation in detecting pests in *** Entropy-ELM method with the Whale Optimization Algorithm(WOA)is used for feature selection,enhancing mango pests’classiﬁcation *** Vector Machines(SVMs)are especially effective for classifying while users get var-ious classes in which they are *** are created as suitable classiﬁers to categorize any dataset in Big Data *** proposed Entropy-ELM-WOA is more capable compared to the existing systems.

关键词： Whale optimization algorithm Entropy-ELM feature selection pests detection support vector machine mango trees classiﬁcation

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Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

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computers, Materials & Continua 2024年第6期79卷 3855-3875页

作者： Maie Aboghazalah Passent El-kafrawy Abdelmoty M.Ahmed Rasha Elnemr Belgacem Bouallegue Ayman El-sayed Math and Computer Science Department Faculty of ScienceMenoufia UniversityShebin El-komEgypt College of Engineering Computer Science DepartmentEffat UniversityJeddahKingdom of Saudi Arabia Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityShebin El-komEgypt Climate Change Information Center and Expert Systems Agriculture Research CenterGizaEgypt

Heart monitoring improves life ***(ECGs or EKGs)detect heart *** learning algorithms can create a few ECG diagnosis processing *** first method uses raw ECG and time-series *** second method classifies the ECG by patient *** third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer *** ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and *** using all three approaches have not been examined till *** researchers found that Machine Learning(ML)techniques can improve ECG *** study will compare popular machine learning techniques to evaluate ECG *** algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization *** plus prior knowledge has the highest accuracy(99%)of the four ML *** characteristics failed to identify signals without chaos *** 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.

关键词： ECG extraction ECG leads time series prior knowledge and arrhythmia chaos theory QRS complex analysis machine learning ECG classification

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