Zainab Nayyar¹, Muhammad Attique Khan¹, Musaed Alhussein², Muhammad Nazir¹, Khursheed Aurangzeb², Yunyoung Nam^3,*, Seifedine Kadry⁴, Syed Irtaza Haider²

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2041-2056, 2021, DOI:10.32604/cmc.2021.015916

Abstract Artificial intelligence aids for healthcare have received a great deal of attention. Approximately one million patients with gastrointestinal diseases have been diagnosed via wireless capsule endoscopy (WCE). Early diagnosis facilitates appropriate treatment and saves lives. Deep learning-based techniques have been used to identify gastrointestinal ulcers, bleeding sites, and polyps. However, small lesions may be misclassified. We developed a deep learning-based best-feature method to classify various stomach diseases evident in WCE images. Initially, we use hybrid contrast enhancement to distinguish diseased from normal regions. Then, a pretrained model is fine-tuned, and further training is done via transfer learning. Deep features are… More >

Kumari Sheeja Saraswathy, Sobha Lalitha Devi^*

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1983-1999, 2021, DOI:10.32604/cmc.2021.015661

Abstract The sentiment of a text depends on the clausal structure of the sentence and the connectives’ discourse arguments. In this work, the clause boundary, discourse argument, and syntactic and semantic information of the sentence are used to assign the text’s sentiment. The clause boundaries identify the span of the text, and the discourse connectives identify the arguments. Since the lexicon-based analysis of traditional sentiment analysis gives the wrong sentiment of the sentence, a deeper-level semantic analysis is required for the correct analysis of sentiments. Hence, in this study, explicit connectives in Malayalam are considered to identify the discourse arguments. A… More >

Muhammad Usman Ghani Khan^1,2, Zubaira Naz¹, Javeria Khan¹, Tanzila Saba³, Ibrahim Abunadi³, Amjad Rehman³, Usman Tariq^4,*

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2363-2376, 2021, DOI:10.32604/cmc.2021.014550

Abstract The National Health Interview Survey (NHIS) shows that there are 13.2% of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder (ADHD), globally. The treatment methods for ADHD are either psycho-stimulant medications or cognitive therapy. These traditional methods, namely therapy, need a large number of visits to hospitals and include medication. Neurogames could be used for the effective treatment of ADHD. It could be a helpful tool in improving children and ADHD patients’ cognitive skills by using Brain–Computer Interfaces (BCI). BCI enables the user to interact with the computer through brain activity using… More >

Jamal Kh-Madhloom^1,2,*, Mohd Khanapi Abd Ghani¹, Mohd Rizuan Baharon¹

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 493-512, 2021, DOI:10.32604/iasc.2021.015129

Abstract Over the decades, protecting the privacy of a health cloud using the design of a fog computing network is a very important field and will be more important in the near future. Current Internet of Things (IoT) research includes security and privacy due to their extreme importance in any growing technology that involves the implementation of cryptographic Internet communications (ICs) for protected IC applications such as fog computing and cloud computing devices. In addition, the implementation of public-key cryptography for IoT-based DNA sequence testing devices requires considerable expertise. Any key can be broken by using a brute-force attack with ample… More >

Jingying Li¹, Tiejun Zhu¹, Fengming Li¹, Dong Wang¹, Xianbiao Bu², Lingbao Wang^2,*

Energy Engineering, Vol.118, No.3, pp. 517-534, 2021, DOI:10.32604/EE.2021.014464

Abstract The single well geothermal heating (SWGH) technology has attracted extensive attention. To enhance heat extraction from SWGH, a mathematical model describing heat transfer is set up, and the key influence factor and heat transfer enhancement method are discussed by thermal resistance analysis. The numerical results show that the thermal resistance of rock is far greater than that of well wall and fluid. So, reducing rock thermal resistance is the most effective method for enhancing the heat extraction power. For geothermal well planning to drill: rock thermal resistance can be reduced by increasing well diameter and rock thermal conductivity; the temperature… More >

Muhammad Adeel Azam¹, Khan Bahadar Khan^2,*, Muhammad Ahmad³, Manuel Mazzara⁴

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 821-840, 2021, DOI:10.32604/cmc.2021.016131

Abstract For the last two decades, physicians and clinical experts have used a single imaging modality to identify the normal and abnormal structure of the human body. However, most of the time, medical experts are unable to accurately analyze and examine the information from a single imaging modality due to the limited information. To overcome this problem, a multimodal approach is adopted to increase the qualitative and quantitative medical information which helps the doctors to easily diagnose diseases in their early stages. In the proposed method, a Multi-resolution Rigid Registration (MRR) technique is used for multimodal image registration while Discrete Wavelet… More >

Ayesha Bin T. Tahir¹, Muhamamd Attique Khan¹, Majed Alhaisoni², Junaid Ali Khan¹, Yunyoung Nam^3,*, Shui-Hua Wang⁴, Kashif Javed⁵

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1099-1116, 2021, DOI:10.32604/cmc.2021.015154

Abstract Background: A brain tumor reflects abnormal cell growth. Challenges: Surgery, radiation therapy, and chemotherapy are used to treat brain tumors, but these procedures are painful and costly. Magnetic resonance imaging (MRI) is a non-invasive modality for diagnosing tumors, but scans must be interpretated by an expert radiologist. Methodology: We used deep learning and improved particle swarm optimization (IPSO) to automate brain tumor classification. MRI scan contrast is enhanced by ant colony optimization (ACO); the scans are then used to further train a pretrained deep learning model, via transfer learning (TL), and to extract features from two dense layers. We fused… More >

Hamid A. Jalab¹, Ala’a R. Al-Shamasneh¹, Hadil Shaiba², Rabha W. Ibrahim^3,4,*, Dumitru Baleanu^5,6,7

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2061-2075, 2021, DOI:10.32604/cmc.2021.015170

Abstract Recently, many rapid developments in digital medical imaging have made further contributions to health care systems. The segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical diagnoses. Many factors like image contrast and quality affect the result of image segmentation. Due to that, image contrast remains a challenging problem for image segmentation. This study presents a new image enhancement model based on fractional Rényi entropy for the segmentation of kidney MRI scans. The proposed work consists of two stages: enhancement by fractional Rényi entropy, and MRI Kidney deep segmentation. The proposed… More >

Ch. Srinivas^1,*, K. Chandrabhushana Rao²

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1801-1817, 2021, DOI:10.32604/cmc.2021.014981

Abstract In this paper we propose an efficient process of physiological artifact elimination methodology from brain waves (BW), which are also commonly known as electroencephalogram (EEG) signal. In a clinical environment during the acquisition of BW several artifacts contaminates the actual BW component. This leads to inaccurate and ambiguous diagnosis. As the statistical nature of the EEG signal is more non-stationery, adaptive filtering is the more promising method for the process of artifact elimination. In clinical conditions, the conventional adaptive techniques require many numbers of computational operations and leads to data samples overlapping and instability of the algorithm used. This causes… More >

Rabia Javed^1,2, Mohd Shafry Mohd Rahim¹, Tanzila Saba³, Suliman Mohamed Fati³, Amjad Rehman^3,*, Usman Tariq⁴

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2337-2352, 2021, DOI:10.32604/cmc.2021.014677

Abstract Most of the melanoma cases of skin cancer are the life-threatening form of cancer. It is prevalent among the Caucasian group of people due to their light skin tone. Melanoma is the second most common cancer that hits the age group of 15–29 years. The high number of cases has increased the importance of automated systems for diagnosing. The diagnosis should be fast and accurate for the early treatment of melanoma. It should remove the need for biopsies and provide stable diagnostic results. Automation requires large quantities of images. Skin lesion datasets contain various kinds of dermoscopic images for the… More >