Meenu Chaudhary¹, Loveleen Gaur¹, NZ Jhanjhi^2,*, Mehedi Masud³, Sultan Aljahdali³

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1009-1024, 2022, DOI:10.32604/iasc.2022.023417

Abstract Employee categorisation differentiates valuable employees as eighty per cent of profit comes from twenty per cent of employees. Also, retention of all employees is quite challenging and incur a cost. Previous studies have focused on employee churn analysis using various machine learning algorithms but have missed the categorisation of an employee based on accomplishments. This paper provides an approach of categorising employees to quantify the importance of the employees using multi-criteria decision making (MCDM) techniques, i.e., criteria importance through inter-criteria correlation (CRITIC) to assign relative weights to employee accomplishments and fuzzy Measurement Alternatives and Ranking according to the Compromise Solution… More >

Kuei-Feng Chang¹, Wen-Goang Yang², Ya-Wen Cheng³, I-Tung Shih^2,*

International Journal of Mental Health Promotion, Vol.24, No.2, pp. 219-236, 2022, DOI:10.32604/ijmhp.2022.018187

Abstract This study explored the mechanism on how volunteers as rationalists use rationalism during their cognitive appraisal process when dealing with emotional events in their social helping behavior such as international rescue events. The authors used the triangulation method to include three studies (Study 1 is a qualitative research which explored ways of TCF leader’s inspiring their volunteer workers; Study 2 is a quantitative research on the decision-making process of volunteer individuals involving in international rescue activities; Study 3 is a quantitative research on individuals’ motivation for joining social helping behavior) for validation of Tzu Chi Foundation (TCF), which is a… More >

Chana Chansri, Jakkree Srinonchat^*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4377-4389, 2022, DOI:10.32604/cmc.2022.024026

Abstract Egocentric recognition is exciting computer vision research by acquiring images and video from the first-person overview. However, an image becomes noisy and dark under low illumination conditions, making subsequent hand detection tasks difficult. Thus, image enhancement is necessary to make buried detail more visible. This article addresses the challenge of egocentric hand grasp recognition in low light conditions by utilizing the flex sensor and image enhancement algorithm based on adaptive gamma correction with weighting distribution. Initially, a flex sensor is installed to the thumb for object manipulation. The thumb placement that holds in a different position on the object of… More >

Labiba Gillani Fahad¹, Syed Fahad Tahir^2,*, Usama Rasheed¹, Hafsa Saqib¹, Mehdi Hassan², Hani Alquhayz³

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5083-5098, 2022, DOI:10.32604/cmc.2022.023357

Abstract The nutritional value of perishable food items, such as fruits and vegetables, depends on their freshness levels. The existing approaches solve a binary class problem by classifying a known fruit\vegetable class into fresh or rotten only. We propose an automated fruits and vegetables categorization approach that first recognizes the class of object in an image and then categorizes that fruit or vegetable into one of the three categories: pure-fresh, medium-fresh, and rotten. We gathered a dataset comprising of 60K images of 11 fruits and vegetables, each is further divided into three categories of freshness, using hand-held cameras. The recognition and… More >

Fuqi Yao¹, Jinwei Sun¹, Jianhua Dong^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 671-700, 2022, DOI:10.32604/cmes.2022.018450

Abstract Accurate estimation of dew point temperature (T_dew) plays a very important role in the fields of water resource management, agricultural engineering, climatology and energy utilization. However, there are few studies on the applicability of local T_dew algorithms at regional scales. This study evaluated the performance of a new machine learning algorithm, i.e., gradient boosting on decision trees with categorical features support (CatBoost) to estimate daily T_dew using limited local and cross-station meteorological data. The random forests (RF) algorithm was also assessed for comparison. Daily meteorological data from 2016 to 2019, including maximum, minimum and average temperature (T_max, T_min and T_mean),… More >

Abdullah Alabdulatif¹, Syed Sajjad Hussain Rizvi^2,*

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 827-840, 2022, DOI:10.32604/iasc.2022.021879

Abstract Network intrusion detection is the pressing need of every communication network. Many network intrusion detection systems (NIDS) have been proposed in the literature to cater to this need. In recent literature, plug-and-play NIDS, Kitsune, was proposed in 2018 and greatly appreciated in the literature. The Kitsune datasets were divided into 70% training set and 30% testing set for machine learning algorithms. Our previous study referred that the variants of the Tree algorithms such as Simple Tree, Medium Tree, Coarse Tree, RUS Boosted, and Bagged Tree have reported similar effectiveness but with slight variation inefficiency. To further extend this investigation, we… More >

Chunyang Zhang¹, Yijun Lin¹, Yuyang Zhou², Wei Xu^1,3,*

International Journal of Mental Health Promotion, Vol.23, No.4, pp. 443-454, 2021, DOI:10.32604/IJMHP.2021.017229

Abstract Left-behind experience refers to the experience of children staying behind in their hometown under the care of only one parent or their relatives while one or both of their parents leave to work in other places. College students with left-behind experience showed higher aggression levels. To further explore the relationship between left-behind experience and aggression, the current study categorized left-behind experience using latent class analysis and explored its relationship with aggression. One thousand twenty-eight Chinese college students with left-behind experience were recruited, and their aggression levels were assessed. The results showed that there were four categories of left-behind experience: “starting… More >

R. Bala Krishnan¹, M. M. Anishin Raj², N. Rajesh Kumar¹, B. Karthikeyan³, G. Manikandan^3,*, N. R. Raajan⁴

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 221-235, 2022, DOI:10.32604/iasc.2022.021775

Abstract In recent years information hiding has got much attention as it acts as an alternate option for secured communication. The Secret content would get imbedded with the image using various possible image embodiment techniques, in which the Least Significant Bit (LSB) substitution is one of the preferred content embodiment strategy; however, asserting the quality and the originality of the content embedded image (stego) is yet a grievous concern in the field of Information Security. In this article, a proficient Scrambling Based Haar Wavelet Transform (SBHWT) approach has been sought to ensure the novelty of the stego-image that supports the safe… More >

LIXU JIANG¹, LIN NING², CHUNCHAO PU¹, ZIXIN WANG¹, BIFANG HE^1,3, JIAN HUANG^1,*

BIOCELL, Vol.46, No.2, pp. 547-557, 2022, DOI:10.32604/biocell.2021.016500

Abstract As nucleic acid-guided endonucleases, some prokaryotic Argonautes have been used as programmable nucleases. Natronobacterium gregoryi Argonaute (NgAgo) has also been proposed for gene editing, but this remains very controversial. Until now, the endogenous nucleic acids that bind to NgAgo in Natronobacterium gregoryi sp2 (N. gregoryi sp2) have not been characterized. We expressed the conserved PIWI domain of NgAgo and used it to induce anti-PIWI antibody. We also cultured the N. gregoryi sp2 strain and performed immunoprecipitation, chromatin immunoprecipitation (ChIP), and RNA immunoprecipitation (RIP) assays. The nucleic acids that endogenously bound NgAgo in N. gregoryi sp2 cells were sequenced and analyzed.… More >

Iman Almomani^1,2,*, Aala AlKhayer¹, Walid El-Shafai^1,3

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1209-1228, 2022, DOI:10.32604/cmc.2022.018631

Abstract Ransomware is considered one of the most threatening cyberattacks. Existing solutions have focused mainly on discriminating ransomware by analyzing the apps themselves, but they have overlooked possible ways of hiding ransomware apps and making them difficult to be detected and then analyzed. Therefore, this paper proposes a novel ransomware hiding model by utilizing a block-based High-Efficiency Video Coding (HEVC) steganography approach. The main idea of the proposed steganography approach is the division of the secret ransomware data and cover HEVC frames into different blocks. After that, the Least Significant Bit (LSB) based Hamming Distance (HD) calculation is performed amongst the… More >