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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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Morlet wavelet transformation based deep similarity structured neural learning for image quality assessment

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Multimedia Tools and Applications 2024年第16期84卷 1-19页

作者： Balakrishnan, N. Robinson, Y. Harold Department of MCA Sona College of Technology Salem India Department of Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India

Quality assessment is a key problem to be resolved in image processing. Few research works have been designed to analyze the quality of images using different techniques. However, the accuracy involved during the process of image quality assessment was not sufficient. A novel method Morlet Wavelet Transformation Based Deep Similarity Structured Neural Learning (MWT-DSSNL) is proposed to enhance the performance of image quality assessment with minimal peak signal-to-noise ratio. The MWT-DSSNL Method is based on artificial neural networks with representation learning. The MWT-DSSNL Method initially gets the number of images from a given dataset as input. The MWT-DSSNL Method uses multiple layers to extract higher-level features from the input images. The MWT-DSSNL Method is a Feed-Forward network where each layer employs the output from the previous layer as input. The MWT-DSSNL Method is designed based on a biological neural network of the human brain. Contrary to the existing method, the MWT-DSSNL Method uses multiple hidden layers and Morlet wavelet transformation to deeply analyze input images and thereby extract features such as luminance, contrast, and structure with a minimal amount of time consumption. By considering the discovered features, finally MWT-DSSNL Method determines structural similarity and thereby exactly identifies the quality of input images with a lower time. From that, the MWT-DSSNL Method achieves enhanced image quality assessment performance when compared to existing works. The simulation of the MWT-DSSNL Method is conducted on factors such as PSNR, average processing time, quality detection accuracy, and false positive rate with different numbers of input images. The simulation result depicts that the MWT-DSSNL method increases the accuracy and also minimizes the time of image quality assessment when compared to conventional works. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024

关键词： Network layers

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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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Enhancing sustainable 5G powered agriculture 4.0 : Summary of low power connectivity, internet of UAV things, AI solutions and research trends

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Multimedia Tools and Applications 2024年第17期84卷 1-45页

作者： Majumdar, Parijata Mitra, Sanjoy Bhattacharya, Diptendu Bhushan, Bharat Department of Computer Science and Engineering Techno College of Engineering Agartala Maheshkhola Madhuban799004 India Department of Computer Science and Engineering Tripura Institute of Technology Agartala Narsingarh Agartala799009 India Department of Computer Science and Engineering National Institute of Technology Agartala Jirania Agartala799046 India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida201310 India

Emerging technologies of Agriculture 4.0 such as the Internet of Things (IoT), Cloud Computing, Artificial Intelligence (AI), and 5G network services are being rapidly deployed to address smart farming implementation-related concerns and to fulfill the increasing food demand of the expanding demographics. Adoption of 5G offers numerous benefits including Ultra-Reliable Low Latency Communication (URLLC) required to reduce communication delays in time-sensitive applications, non-public networks to allocate part of the frequency spectrum on demand, and network slicing to fulfill the diverse requirements requested by a specific application etc. for realizing the full potential of Agriculture 4.0 operations. In this paper, a comprehensive review of 5G radio enhancements, enabling technologies, and multi-tier heterogeneous 5G network architecture are presented. The main focus is to concisely cover AI integration with Unmanned Aerial Vehicles (UAVs) leveraging 5G technologies and Low Power Wide Area Networks (LPWANs) for supporting intelligent, long-range, cost-effective and energy-saving data transmission across the network and seamless Agriculture 4.0 operations. Cloud computing architectures that permit centralized management of Agriculture 4.0 operations through sharing of computations, configurable networks, and servers are also studied in detail to reveal the need for edge computation as increasing number of IoT devices allows only to perform complex computations on-site. Extensive survey reveals the recent advances of 5G-enabled Agriculture 4.0 operations, future research opportunities of 5G networks including Energy Harvesting techniques for the deployment of massive to critical IoT applications, and the socio-demographic impacts of 5G networks in Agriculture 4.0 to support ubiquitous connectivity of exponentially increasing IoT devices. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Energy efficiency

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An optimized deep network-based intermediate code generation for the mathematical expression

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Multimedia Tools and Applications 2024年第17期84卷 1-24页

作者： Sharada, H.N. Anami, Basavaraj Allagi, Shridhar Department of Computer Science & amp Engineering SDM College of Engineering and Technology Karnataka Dharwad580002 India Department of Computer Science & amp Engineering KLE Institute of Technology Karnataka Hubli580030 India

In recent times, the system's mathematical expression and operation have gained greater reach in engineering and mathematics. It is vital to solving more complex expressions and equations in a short time. The most existing methodology was degraded in the recognition performance and provided inaccurate output. Therefore, a novel Squirrel-based Spiking Neural Model (SbSNM) was presented for the intermediate code generation from the mathematical expression relation data. Initially, the input ME relation data was preprocessed to remove noise and tokenized by the spiking neural features. The tokenized expressions were arranged into a syntax tree structure, and then intermediate code was generated by the squirrel functions. Finally, the differentiation was calculated and given as output. Subsequently, the performance parameters were evaluated for the presented SbSNM. The overall accuracy obtained by the presented model is 99.25% which is higher than the other existing techniques and also brought a higher precision and recall rate and f1-score. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Neural networks

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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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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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Security to text (S2T): multi-layered based security approaches for secret text content

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Multimedia Tools and Applications 2025年第15期84卷 15151-15171页

作者： Kashid, Shamal Awasthi, Lalit K. Berwal, Krishan Department of Computer Science and Engineering National Institute of Technology Uttarakhand IN Srinagar India Faculty of Communication Engineering Military College of Telecommunication Engineering IN Mhow India

In the digital world, text data is produced in an unstructured manner across various communication channels. Extracting valuable information from such data with security is crucial and requires the development of techniques in text mining, information retrieval, and natural language processing (NLP). To solve this issue, we introduce two novel approaches: keyword extraction (KE) and a multi-layered secret sharing scheme (MLSS) to provide security to extracted keywords rather than overall text documents. The KE approach encompasses a sequence of text pre-processing procedures, including tokenization, stopword removal, stemming, and bag of words representation, followed by indexing. This methodology aims to efficiently extract keywords from text datasets. For this research work, we have proposed three datasets, including text messages, whatsapp messages, and electronic mail. MLSS enhances the security of extracted textual data by leveraging text pre-processing steps. This scheme ensures better confidentiality and the non-revealment of sensitive information. Additionally, we evaluate our KE model on our dataset as well as on standard datasets. Experimental results demonstrate the effectiveness of our proposed security to text (S2T) model, which outperform existing state-of-the-art approaches. The model obtains a 100% correlation between the reconstructed text and the original text. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Extraction

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