Li Li¹, Chen Cui^1,2, Jianfeng Lu¹, Shanqing Zhang^1,*, Ching-Chun Chang³

Computer Systems Science and Engineering, Vol.38, No.3, pp. 291-303, 2021, DOI:10.32604/csse.2021.016340

Abstract As an efficient technique for anti-counterfeiting, holographic diffraction labels has been widely applied to various fields. Due to their unique feature, traditional image recognition algorithms are not ideal for the holographic diffraction label recognition. Since a tensor preserves the spatiotemporal features of an original sample in the process of feature extraction, in this paper we propose a new holographic diffraction label recognition algorithm that combines two tensor features. The HSV (Hue Saturation Value) tensor and the HOG (Histogram of Oriented Gradient) tensor are used to represent the color information and gradient information of holographic diffraction label, respectively. Meanwhile, the tensor… More >

Pu Han^1,2, Mingtao Zhang¹, Jin Shi³, Jinming Yang⁴, Xiaoyan Li^5,*

Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 55-72, 2021, DOI:10.32604/iasc.2021.017021

Abstract With the rapid development of Internet, the medical Q&A community has become an important channel for people to obtain and share medical and health knowledge. Online medical entity recognition (OMER), as the foundation of medical and health information extraction, has attracted extensive attention of researchers in recent years. In order to further improve the research progress of Chinese OMER, LSTM-Att-Med model is proposed in this paper to capture more external semantic features and important information. First, Word2vec is used to generate the character-level vectors with semantic features on the basis of the unlabeled corpus in the medical domain and open… More >

Muhammad Attique Khan¹, Majed Alhaisoni², Ammar Armghan³, Fayadh Alenezi³, Usman Tariq⁴, Yunyoung Nam^5,*, Tallha Akram⁶

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3841-3859, 2021, DOI:10.32604/cmc.2021.016864

Abstract Human action recognition (HAR) is an essential but challenging task for observing human movements. This problem encompasses the observations of variations in human movement and activity identification by machine learning algorithms. This article addresses the challenges in activity recognition by implementing and experimenting an intelligent segmentation, features reduction and selection framework. A novel approach has been introduced for the fusion of segmented frames and multi-level features of interests are extracted. An entropy-skewness based features reduction technique has been implemented and the reduced features are converted into a codebook by serial based fusion. A custom made genetic algorithm is implemented on… More >

Saleem Ibraheem Saleem^*, Adnan Mohsin Abdulazeez

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3353-3372, 2021, DOI:10.32604/cmc.2021.016342

Abstract Writer identification (WI) based on handwritten text structures is typically focused on digital characteristics, with letters/strokes representing the information acquired from the current research in the integration of individual writing habits/styles. Previous studies have indicated that a word’s attributes contribute to greater recognition than the attributes of a character or stroke. As a result of the complexity of Arabic handwriting, segmenting and separating letters and strokes from a script poses a challenge in addition to WI schemes. In this work, we propose new texture features for WI based on text. The histogram of oriented gradient (HOG) features are modified to… More >

D. Venugopal¹, T. Jayasankar², Mohamed Yacin Sikkandar³, Mohamed Ibrahim Waly³, Irina V. Pustokhina⁴, Denis A. Pustokhin⁵, K. Shankar^6,*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2877-2893, 2021, DOI:10.32604/cmc.2021.015480

Abstract Data fusion is one of the challenging issues, the healthcare sector is facing in the recent years. Proper diagnosis from digital imagery and treatment are deemed to be the right solution. Intracerebral Haemorrhage (ICH), a condition characterized by injury of blood vessels in brain tissues, is one of the important reasons for stroke. Images generated by X-rays and Computed Tomography (CT) are widely used for estimating the size and location of hemorrhages. Radiologists use manual planimetry, a time-consuming process for segmenting CT scan images. Deep Learning (DL) is the most preferred method to increase the efficiency of diagnosing ICH. In… More >

T. Jayasree^1,*, S.Emerald Shia²

Sound & Vibration, Vol.55, No.2, pp. 141-161, 2021, DOI:10.32604/sv.2021.011734

Abstract This paper presents the pathological voice detection and classification techniques using signal processing based methodologies and Feed Forward Neural Networks (FFNN). The important pathological voices such as Autism Spectrum Disorder (ASD) and Down Syndrome (DS) are considered for analysis. These pathological voices are known to manifest in different ways in the speech of children and adults. Therefore, it is possible to discriminate ASD and DS children from normal ones using the acoustic features extracted from the speech of these subjects. The important attributes hidden in the pathological voices are extracted by applying different signal processing techniques. In this work, three… More >

Mingyang Duan, Jin Liu^*, Shiqi Lv

Journal on Big Data, Vol.3, No.2, pp. 77-83, 2021, DOI:10.32604/jbd.2021.016674

Abstract Image caption generation is an essential task in computer vision and image understanding. Contemporary image caption generation models usually use the encoder-decoder model as the underlying network structure. However, in the traditional Encoder-Decoder architectures, only the global features of the images are extracted, while the local information of the images is not well utilized. This paper proposed an Encoder-Decoder model based on fused features and a novel mechanism for correcting the generated caption text. We use VGG16 and Faster R-CNN to extract global and local features in the encoder first. Then, we train the bidirectional LSTM network with the fused… More >

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 >

Zilin Li, Junjiang Wang, Yan Yang^*

Molecular & Cellular Biomechanics, Vol.18, No.2, pp. 87-97, 2021, DOI:10.32604/mcb.2021.014731

Abstract Blocking technology is one of the most important means to obtain score in volleyball, which has a great influence on the victory of the game. In this study, the vertical jump was analyzed to understand the movement characteristics of legs during training time. The kinematics and dynamics data of the legs of 10 volleyball players after training for 0 h, 0.5 h, 1 h and 2 h were studied by using an infrared light point motion capture system and force measuring platform. The results showed that the joint angles of the players who had 1 h and 2 h of… More >

Bozhi Xu¹, Jiarui Liu¹, Jifan Liang¹, Wei Lu^1,*, Yue Zhang²

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1375-1388, 2021, DOI:10.32604/cmc.2021.016760

Abstract In recent years, with the rapid development of deep learning technologies, some neural network models have been applied to generate fake media. DeepFakes, a deep learning based forgery technology, can tamper with the face easily and generate fake videos that are difficult to be distinguished by human eyes. The spread of face manipulation videos is very easy to bring fake information. Therefore, it is important to develop effective detection methods to verify the authenticity of the videos. Due to that it is still challenging for current forgery technologies to generate all facial details and the blending operations are used in… More >