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Search Results (11)
  • Open Access


    An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks

    Reem Alkanhel1, Kalaiselvi Chinnathambi2, C. Thilagavathi3, Mohamed Abouhawwash4,5, Mona A. Al duailij6, Manal Abdullah Alohali7, Doaa Sami Khafaga6,*

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1571-1583, 2023, DOI:10.32604/iasc.2023.033430

    Abstract Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the surroundings. Designing energy-efficient data gathering methods in large-scale Wireless Sensor Networks (WSN) is one of the most difficult areas of study. As every sensor node has a finite amount of energy. Battery power is the most significant source in the WSN. Clustering is a well-known technique for enhancing the power feature in WSN. In the proposed method multi-Swarm optimization based on a Genetic Algorithm and Adaptive Hierarchical clustering-based routing protocol are… More >

  • Open Access


    Incremental Linear Discriminant Analysis Dimensionality Reduction and 3D Dynamic Hierarchical Clustering WSNs

    G. Divya Mohana Priya1,*, M. Karthikeyan1, K. Murugan2

    Computer Systems Science and Engineering, Vol.43, No.2, pp. 471-486, 2022, DOI:10.32604/csse.2022.021023

    Abstract Optimizing the sensor energy is one of the most important concern in Three-Dimensional (3D) Wireless Sensor Networks (WSNs). An improved dynamic hierarchical clustering has been used in previous works that computes optimum clusters count and thus, the total consumption of energy is optimal. However, the computational complexity will be increased due to data dimension, and this leads to increase in delay in network data transmission and reception. For solving the above-mentioned issues, an efficient dimensionality reduction model based on Incremental Linear Discriminant Analysis (ILDA) is proposed for 3D hierarchical clustering WSNs. The major objective of… More >

  • Open Access


    Agro-Morphological Characterization and Genetic Dissection of Linseed (Linum usitatissimum L.) Genotypes

    A. K. M. Golam Sarwar1, Md. Sabibul Haque1,*, Md. Ekramul Haque2, Md. Amir Hossain3, Md. Golam Azam4, Md. Nesar Uddin1, Eldessoky S. Dessoky5, Mahmoud A. Basry6, Md. Alamgir Hossain1

    Phyton-International Journal of Experimental Botany, Vol.91, No.8, pp. 1721-1743, 2022, DOI:10.32604/phyton.2022.021069

    Abstract Linseed is a multipurpose crop and the crop needs further improvement to increase production and yield due to its high value and demand. This study aimed to assess the extent and pattern of genetic variability of forty linseed genotypes based on diverse agro–morphological and yield attributes. The field experiment was conducted following a Randomized Complete Block Design with three replications. Linseed germplasm showed a wide range of phenotypic expression, genetic variability and heritability for 30 studied traits. A low to high phenotypic coeffi- cient of variation (PCV) and genotypic coefficient of variation (GCV) were observed.… More >

  • Open Access


    Requirements Engineering: Conflict Detection Automation Using Machine Learning

    Hatim Elhassan1, Mohammed Abaker1, Abdelzahir Abdelmaboud2, Mohammed Burhanur Rehman1,*

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 259-273, 2022, DOI:10.32604/iasc.2022.023750

    Abstract The research community has well recognized the importance of requirement elicitation. Recent research has shown the continuous decreasing success rate of IS projects in the last five years due to the complexity of the requirement conflict refinement process. Requirement conflict is at the heart of requirement elicitation. It is also considered the prime reason for deciding the success or failure of the intended Information System (IS) project. This paper introduces the requirements conflict detection automation model based on the Mean shift clustering unsupervised machine learning model. It utilizes the advantages of Artificial Intelligence in detecting… More >

  • Open Access


    COVID19 Outbreak: A Hierarchical Framework for User Sentiment Analysis

    Ahmed F. Ibrahim1, M. Hassaballah2, Abdelmgeid A. Ali3, Yunyoung Nam4,*, Ibrahim A. Ibrahim3

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2507-2524, 2022, DOI:10.32604/cmc.2022.018131

    Abstract Social networking sites in the most modernized world are flooded with large data volumes. Extracting the sentiment polarity of important aspects is necessary; as it helps to determine people’s opinions through what they write. The Coronavirus pandemic has invaded the world and been given a mention in the social media on a large scale. In a very short period of time, tweets indicate unpredicted increase of coronavirus. They reflect people’s opinions and thoughts with regard to coronavirus and its impact on society. The research community has been interested in discovering the hidden relationships from short… More >

  • Open Access


    Hierarchical Stream Clustering Based NEWS Summarization System

    M. Arun Manicka Raja1,*, S. Swamynathan2

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1263-1280, 2022, DOI:10.32604/cmc.2022.019451

    Abstract News feed is one of the potential information providing sources which give updates on various topics of different domains. These updates on various topics need to be collected since the domain specific interested users are in need of important updates in their domains with organized data from various sources. In this paper, the news summarization system is proposed for the news data streams from RSS feeds and Google news. Since news stream analysis requires live content, the news data are continuously collected for our experimentation. The major contributions of this work involve domain corpus based… More >

  • Open Access


    An Intelligent Gestational Diabetes Diagnosis Model Using Deep Stacked Autoencoder

    A. Sumathi1,*, S. Meganathan1, B. Vijila Ravisankar2

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3109-3126, 2021, DOI:10.32604/cmc.2021.017612

    Abstract Gestational Diabetes Mellitus (GDM) is one of the commonly occurring diseases among women during pregnancy. Oral Glucose Tolerance Test (OGTT) is followed universally in the diagnosis of GDM diagnosis at early pregnancy which is costly and ineffective. So, there is a need to design an effective and automated GDM diagnosis and classification model. The recent developments in the field of Deep Learning (DL) are useful in diagnosing different diseases. In this view, the current research article presents a new outlier detection with deep-stacked Autoencoder (OD-DSAE) model for GDM diagnosis and classification. The goal of the… More >

  • Open Access


    Driving Pattern Profiling and Classification Using Deep Learning

    Meenakshi Malik1, Rainu Nandal1, Surjeet Dalal2, Vivek Jalglan3, Dac-Nhuong Le4,5,*

    Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 887-906, 2021, DOI:10.32604/iasc.2021.016272

    Abstract The last several decades have witnessed an exponential growth in the means of transport globally, shrinking geographical distances and connecting the world. The automotive industry has grown by leaps and bounds, with millions of new vehicles being sold annually, be it for personal commuting or for public or commodity transport. However, millions of motor vehicles on the roads also mean an equal number of drivers with varying levels of skill and adherence to safety regulations. Very little has been done in the way of exploring and profiling driving patterns and vehicular usage using real world… More >

  • Open Access


    Urdu Ligature Recognition System: An Evolutionary Approach

    Naila Habib Khan1,*, Awais Adnan1, Abdul Waheed2,3, Mahdi Zareei4, Abdallah Aldosary5, Ehab Mahmoud Mohamed6,7

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1347-1367, 2021, DOI:10.32604/cmc.2020.013715

    Abstract Cursive text recognition of Arabic script-based languages like Urdu is extremely complicated due to its diverse and complex characteristics. Evolutionary approaches like genetic algorithms have been used in the past for various optimization as well as pattern recognition tasks, reporting exceptional results. The proposed Urdu ligature recognition system uses a genetic algorithm for optimization and recognition. Overall the proposed recognition system observes the processes of pre-processing, segmentation, feature extraction, hierarchical clustering, classification rules and genetic algorithm optimization and recognition. The pre-processing stage removes noise from the sentence images, whereas, in segmentation, the sentences are segmented More >

  • Open Access


    A Direct Data-Cluster Analysis Method Based on Neutrosophic Set Implication

    Sudan Jha1, Gyanendra Prasad Joshi2, Lewis Nkenyereya3, Dae Wan Kim4, *, Florentin Smarandache5

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1203-1220, 2020, DOI:10.32604/cmc.2020.011618

    Abstract Raw data are classified using clustering techniques in a reasonable manner to create disjoint clusters. A lot of clustering algorithms based on specific parameters have been proposed to access a high volume of datasets. This paper focuses on cluster analysis based on neutrosophic set implication, i.e., a k-means algorithm with a threshold-based clustering technique. This algorithm addresses the shortcomings of the k-means clustering algorithm by overcoming the limitations of the threshold-based clustering algorithm. To evaluate the validity of the proposed method, several validity measures and validity indices are applied to the Iris dataset (from the University More >

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