Asadullah Shaikh^1,*, Syed Rizwan², Abdullah Alghamdi¹, Noman Islam², M.A. Elmagzoub¹, Darakhshan Syed²

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1529-1546, 2022, DOI:10.32604/iasc.2022.021163

Abstract Fault localization is one of the main tasks of software debugging. Developers spend a lot of time, cost, and effort to locate the faults correctly manually. For reducing this effort, many automatic fault localization techniques have been proposed, which inputs test suites and outputs a sorted list of faulty entities of the program. For further enhancement in this area, we developed a system called SILearning, which is based on invariant analysis. It learns from some existing fixed bugs to locate faulty methods in the program. It combines machine-learned ranking, program invariant differences, and spectrum-based fault localization (SBFL). Using the execution… More >

Akanksha Tiwari¹, Ram Bilas Pachori^1,2, Premjit Khanganba Sanjram^1,3,4,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2347-2363, 2022, DOI:10.32604/cmc.2022.020299

Abstract In performing a gaming task, mental rotation (MR) is one of the important aspects of visuospatial processing. MR involves dorsal-ventral pathways of the brain. Visual objects/models used in computer-games play a crucial role in gaming experience of the users. The visuospatial characteristics of the objects used in the computer-game influence the engagement of dorsal-ventral visual pathways. The current study investigates how the objects’ visuospatial characteristics (i.e., angular disparity and dimensionality) in an MR-based computer-game influence the cortical activities in dorsal-ventral visual pathways. Both the factors have two levels, angular disparity: convex angle (CA) vs. reflex angle (RA) and dimensionality: 2D… More >

Tuğrul Çavdar¹, Faruk Baturalp Günay^2,*, Nader Ebrahimpour¹, Muhammet Talha Kakız³

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1197-1222, 2022, DOI:10.32604/iasc.2022.020127

Abstract Localization is an essential task in Wireless Sensor Networks (WSN) for various use cases such as target tracking and object monitoring. Anchor nodes play a critical role in this task since they can find their location via GPS signals or manual setup mechanisms and help other nodes in the network determine their locations. Therefore, the optimal placement of anchor nodes in a WSN is of particular interest for reducing the energy consumption while yielding better accuracy at finding locations of the nodes. In this paper, we propose a novel approach for finding the optimal number of anchor nodes and an… More >

Gagandeep Singh Walia¹, Parulpreet Singh¹, Manwinder Singh¹, Mohamed Abouhawwash^2,3, Hyung Ju Park⁴, Byeong-Gwon Kang^4,*, Shubham Mahajan⁵, Amit Kant Pandit⁵

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 305-321, 2022, DOI:10.32604/cmc.2022.019171

Abstract Location information plays an important role in most of the applications in Wireless Sensor Network (WSN). Recently, many localization techniques have been proposed, while most of these deals with two Dimensional applications. Whereas, in Three Dimensional applications the task is complex and there are large variations in the altitude levels. In these 3D environments, the sensors are placed in mountains for tracking and deployed in air for monitoring pollution level. For such applications, 2D localization models are not reliable. Due to this, the design of 3D localization systems in WSNs faces new challenges. In this paper, in order to find… More >

Suma Sira Jacob^1,*, K. Muthumayil², M. Kavitha³, Lijo Jacob Varghese⁴, M. Ilayaraja⁵, Irina V. Pustokhina⁶, Denis A. Pustokhin⁷

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1229-1245, 2022, DOI:10.32604/cmc.2022.019019

Abstract Wireless sensor network (WSN) is an emerging technology which find useful in several application areas such as healthcare, environmental monitoring, border surveillance, etc. Several issues that exist in the designing of WSN are node localization, coverage, energy efficiency, security, and so on. In spite of the issues, node localization is considered an important issue, which intends to calculate the coordinate points of unknown nodes with the assistance of anchors. The efficiency of the WSN can be considerably influenced by the node localization accuracy. Therefore, this paper presents a modified search and rescue optimization based node localization technique (MSRO-NLT) for WSN.… More >

Xiaoyan Zhao^1,*, Lin Zhou², Ying Tong¹, Yuxiao Qi¹, Jingang Shi³

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 361-371, 2021, DOI:10.32604/iasc.2021.018823

Abstract In order to improve the performance of microphone array-based sound source localization (SSL), a robust SSL algorithm using convolutional neural network (CNN) is proposed in this paper. The Gammatone sub-band steered response power-phase transform (SRP-PHAT) spatial spectrum is adopted as the localization cue due to its feature correlation of consecutive sub-bands. Since CNN has the “weight sharing” characteristics and the advantage of processing tensor data, it is adopted to extract spatial location information from the localization cues. The Gammatone sub-band SRP-PHAT spatial spectrum are calculated through the microphone signals decomposed in frequency domain by Gammatone filters bank. The proposed algorithm… More >

Imran Ashraf, Soojung Hur, Yousaf Bin Zikria, Yongwan Park^*

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2597-2620, 2021, DOI:10.32604/cmc.2021.016214

Abstract Predominantly the localization accuracy of the magnetic field-based localization approaches is severed by two limiting factors: Smartphone heterogeneity and smaller data lengths. The use of multifarious smartphones cripples the performance of such approaches owing to the variability of the magnetic field data. In the same vein, smaller lengths of magnetic field data decrease the localization accuracy substantially. The current study proposes the use of multiple neural networks like deep neural network (DNN), long short term memory network (LSTM), and gated recurrent unit network (GRN) to perform indoor localization based on the embedded magnetic sensor of the smartphone. A voting scheme… More >

Salisu Muhammed^*, Erbuğ Çelebi

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 397-416, 2021, DOI:10.32604/iasc.2021.016574

Abstract As the models with fewer operations help realize the performance of intelligent computing systems, we propose a novel deep network for DeepGait feature extraction with less operation for video sensor-based gait representation without dimension decomposition. The DeepGait has been known to have outperformed the hand-crafted representations, such as the frequency-domain feature (FDF), gait energy image (GEI), and gait flow image (GFI), etc. More explicitly, the channel-activated mapping network (CAMNet) is composed of three progressive triplets of convolution, batch normalization, max-pooling layers, and an external max pooling to capture the Spatio-temporal information of multiple frames in one gait period. We conducted… More >

Ahmed Silik^1,2, Mohammad Noori^3,*, Wael A. Altabey^1,4, Ramin Ghiasi¹, Zhishen Wu¹

Structural Durability & Health Monitoring, Vol.15, No.1, pp. 1-22, 2021, DOI:10.32604/sdhm.2021.012751

Abstract A critical problem facing data collection in structural health monitoring, for instance via sensor networks, is how to extract the main components and useful features for damage detection. A structural dynamic measurement is more often a complex time-varying process and therefore, is prone to dynamic changes in time-frequency contents. To extract the signal components and capture the useful features associated with damage from such non-stationary signals, a technique that combines the time and frequency analysis and shows the signal evolution in both time and frequency is required. Wavelet analyses have proven to be a viable and effective tool in this… More >

Muhammad Attique Khan¹, Tallha Akram², Muhammad Sharif¹, Seifedine Kadry³, Yunyoung Nam^4,*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1041-1064, 2021, DOI:10.32604/cmc.2021.016307

Abstract In this work, we propose a new, fully automated system for multiclass skin lesion localization and classification using deep learning. The main challenge is to address the problem of imbalanced data classes, found in HAM10000, ISBI2018, and ISBI2019 datasets. Initially, we consider a pre-trained deep neural network model, DarkeNet19, and fine-tune the parameters of third convolutional layer to generate the image gradients. All the visualized images are fused using a High-Frequency approach along with Multilayered Feed-Forward Neural Network (HFaFFNN). The resultant image is further enhanced by employing a log-opening based activation function to generate a localized binary image. Later, two… More >