Naglaa F. El Abady^1,*, Mohamed Taha¹, Hala H. Zayed^1,2

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1417-1436, 2022, DOI:10.32604/cmc.2022.028044

Abstract Because of the widespread availability of low-cost printers and scanners, document forgery has become extremely popular. Watermarks or signatures are used to protect important papers such as certificates, passports, and identification cards. Identifying the origins of printed documents is helpful for criminal investigations and also for authenticating digital versions of a document in today’s world. Source printer identification (SPI) has become increasingly popular for identifying frauds in printed documents. This paper provides a proposed algorithm for identifying the source printer and categorizing the questioned document into one of the printer classes. A dataset of 1200 papers from 20 distinct (13)… More >

Sridharan Kannan^*

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 677-694, 2022, DOI:10.32604/cmes.2022.018580

Abstract With the worldwide analysis, heart disease is considered a significant threat and extensively increases the mortality rate. Thus, the investigators mitigate to predict the occurrence of heart disease in an earlier stage using the design of a better Clinical Decision Support System (CDSS). Generally, CDSS is used to predict the individuals’ heart disease and periodically update the condition of the patients. This research proposes a novel heart disease prediction system with CDSS composed of a clustering model for noise removal to predict and eliminate outliers. Here, the Synthetic Over-sampling prediction model is integrated with the cluster concept to balance the… More >

Muhammad Irfan^1,*, Nasir Ayub², Faisal Althobiani³, Zain Ali⁴, Muhammad Idrees⁵, Saeed Ullah², Saifur Rahman¹, Abdullah Saeed Alwadie¹, Saleh Mohammed Ghonaim³, Hesham Abdushkour³, Fahad Salem Alkahtani¹, Samar Alqhtani⁶, Piotr Gas⁷

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 2141-2158, 2022, DOI:10.32604/cmc.2022.025466

Abstract One of the major concerns for the utilities in the Smart Grid (SG) is electricity theft. With the implementation of smart meters, the frequency of energy usage and data collection from smart homes has increased, which makes it possible for advanced data analysis that was not previously possible. For this purpose, we have taken historical data of energy thieves and normal users. To avoid imbalance observation, biased estimates, we applied the interpolation method. Furthermore, the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing. By proposing an improved version… More >

R. Sujatha¹, B. Venkata Siva Krishna¹, Jyotir Moy Chatterjee², P. Rahul Naidu¹, NZ Jhanjhi^3,*, Challa Charita¹, Eza Nerin Mariya¹, Mohammed Baz⁴

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 525-541, 2022, DOI:10.32604/iasc.2022.021216

Abstract In the current times, COVID-19 has taken a handful of people’s lives. So, vaccination is crucial for everyone to avoid the spread of the disease. However, not every vaccine will be perfect or will get success for everyone. In the present work, we have analyzed the data from the Vaccine Adverse Event Reporting System and understood that the vaccines given to the people might or might not work considering certain demographic factors like age, gender, and multiple other variables like the state of living, etc. This variable is considered because it explains the unmentioned variables like their food habits and… More >

Mohammad Atrees^*, Ashraf Ahmad, Firas Alghanim

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1405-1422, 2022, DOI:10.32604/iasc.2022.020229

Abstract A malicious URL is a link that is created to spread spams, phishing, malware, ransomware, spyware, etc. A user may download malware that can adversely affect the computer by clicking on an infected URL, or might be convinced to provide confidential information to a fraudulent website causing serious losses. These threats must be identified and handled in a decent time and in an effective way. Detection is traditionally done through the blacklist usage method, which relies on keyword matching with previously known malicious domain names stored in a repository. This method is fast and easy to implement, with the advantage… More >

Sherif S. M. Ghoneim^1,*, Amr E. Rashed², Nagy I. Elkalashy¹

Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 467-479, 2021, DOI:10.32604/iasc.2021.016681

Abstract This study uses several artificial intelligence approaches to detect and estimate electrical faults in photovoltaic (PV) farms. The fault detection approaches of random forest, logistic regression, naive Bayes, AdaBoost, and CN2 rule induction were selected from a total of 12 techniques because they produced better decisions for fault detection. The proposed techniques were designed using distributed PV current measurements, plant current, plant voltage, and power. Temperature, radiation, and fault resistance were treated randomly. The proposed classification model was created using the Orange platform. A classification tree was visualized, consisting of seven nodes and four leaves, with a depth of four… More >

Anshu Parashar^*, Nidhi Kalra, Jaskirat Singh, Raman Kumar Goyal

Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 669-682, 2021, DOI:10.32604/iasc.2021.017478

Abstract Electrophysiological (EEG) signals provide good temporal resolution and can be effectively used to assess and diagnose children with Attention Deficit Hyperactivity Disorder (ADHD). This study aims to develop a machine learning model to classify children with ADHD and Healthy Controls. In this study, EEG signals captured under cognitive tasks were obtained from an open-access database of 60 children with ADHD and 60 Healthy Controls children of similar age. The regional contributions towards attaining higher accuracy are identified and further tested using three classifiers: AdaBoost, Random Forest and Support Vector Machine. The EEG data from 19 channels is taken as input… More >

Diantao Hu, Cong Zhang^*, Wenqi Cao, Xintao Lv, Songwu Xie

Journal on Big Data, Vol.3, No.2, pp. 65-76, 2021, DOI:10.32604/jbd.2021.016317

Abstract Grain yield security is a basic national policy of China, and changes in grain yield are influenced by a variety of factors, which often have a complex, non-linear relationship with each other. Therefore, this paper proposes a Grey Relational Analysis–Adaptive Boosting–Support Vector Regression (GRAAdaBoost-SVR) model, which can ensure the prediction accuracy of the model under small sample, improve the generalization ability, and enhance the prediction accuracy. SVR allows mapping to high-dimensional spaces using kernel functions, good for solving nonlinear problems. Grain yield datasets generally have small sample sizes and many features, making SVR a promising application for grain yield datasets.… More >

K. Lakshminarayanan¹, N. Muthukumaran¹, Y. Harold Robinson², Vimal Shanmuganathan³, Seifedine Kadry⁴, Yunyoung Nam^5,*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3045-3055, 2021, DOI:10.32604/cmc.2021.014370

Abstract Hookworm is an illness caused by an internal sponger called a roundworm. Inferable from deprived cleanliness in the developing nations, hookworm infection is a primary source of concern for both motherly and baby grimness. The current framework for hookworm detection is composed of hybrid convolutional neural networks; explicitly an edge extraction framework alongside a hookworm classification framework is developed. To consolidate the cylindrical zones obtained from the edge extraction framework and the trait map acquired into the hookworm scientific categorization framework, pooling layers are proposed. The hookworms display different profiles, widths, and bend directions. These challenges make it difficult for… More >

Hongtao Bai^{1, 2}, Xuan Li^{1, 2}, Lili He^{1, 2}, Longhai Jin^{1, 2}, Chong Wang^{1, 2, 3}, Yu Jiang^{1, 2, *}

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1591-1603, 2020, DOI:10.32604/cmc.2020.09981

Abstract A current problem in diet recommendation systems is the matching of food preferences with nutritional requirements, taking into account individual characteristics, such as body weight with individual health conditions, such as diabetes. Current dietary recommendations employ association rules, content-based collaborative filtering, and constraint-based methods, which have several limitations. These limitations are due to the existence of a special user group and an imbalance of non-simple attributes. Making use of traditional dietary recommendation algorithm researches, we combine the Adaboost classifier with probabilistic matrix factorization. We present a personalized diet recommendation algorithm by taking advantage of probabilistic matrix factorization via Adaboost. A… More >