Reem Alkanhel¹, Kalaiselvi Chinnathambi², C. Thilagavathi³, Mohamed Abouhawwash^4,5, Mona A. Al duailij⁶, Manal Abdullah Alohali⁷, Doaa Sami Khafaga^6,*

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 used for enhancing the network’s… More >

G. Divya Mohana Priya^1,*, 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 the proposed work is to… More >

A. K. M. Golam Sarwar¹, Md. Sabibul Haque^1,*, 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. The lowest genotypic (σ² g)… More >

Hatim Elhassan¹, Mohammed Abaker¹, Abdelzahir Abdelmaboud², Mohammed Burhanur Rehman^1,*

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 and classifying the requirement conflicts… More >

Ahmed F. Ibrahim¹, M. Hassaballah², Abdelmgeid A. Ali³, Yunyoung Nam^4,*, 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 texts such as Twitter and… More >

M. Arun Manicka Raja^1,*, 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 news collection, news content extraction,… More >

A. Sumathi^1,*, 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 proposed OD-DSAE model is to… More >

Meenakshi Malik¹, Rainu Nandal¹, Surjeet Dalal², Vivek Jalglan³, Dac-Nhuong Le^4,5,*

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 data. This paper focuses on… More >

Naila Habib Khan^1,*, Awais Adnan¹, Abdul Waheed^2,3, Mahdi Zareei⁴, Abdallah Aldosary⁵, Ehab Mahmoud Mohamed^6,7

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 into ligature components. Fifteen features… More >

Sudan Jha¹, Gyanendra Prasad Joshi², Lewis Nkenyereya³, Dae Wan Kim^{4, *}, 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 of California, Irvine, Machine… More >