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29,939 Article Results

Deep segmentation of the liver and the hepatic tumors from abdomen tomography images

10.11591/ijece.v12i1.pp303-310
Nermeen Elmenabawy , Mervat El-Seddek , Hossam El-Din Moustafa , Ahmed Elnakib
A pipelined framework is proposed for accurate, automated, simultaneous segmentation of the liver as well as the hepatic tumors from computed tomography (CT) images. The introduced framework composed of three pipelined levels. First, two different transfers deep convolutional neural networks (CNN) are applied to get high-level compact features of CT images. Second, a pixel-wise classifier is used to obtain two output-classified maps for each CNN model. Finally, a fusion neural network (FNN) is used to integrate the two maps. Experimentations performed on the MICCAI’2017 database of the liver tumor segmentation (LITS) challenge, result in a dice similarity coefficient (DSC) of 93.5% for the segmentation of the liver and of 74.40% for the segmentation of the lesion, using a 5-fold cross-validation scheme. Comparative results with the state-of-the-art techniques on the same data show the competing performance of the proposed framework for simultaneous liver and tumor segmentation.
Volume: 12
Issue: 1
Page: 303-310
Publish at: 2022-02-01

Modeling and characterization of optimal nano-scale channel dimensions for fin field effect transistor based on constituent semiconductor materials

10.12928/telkomnika.v20i1.21671
Waheb A.; Universiti Malaysia Pahang Jabbara , Ahmed; Universiti Malaysia Pahang Mahmood , Jamil; Sana’a Community College (SCC) Sultan
This study aims to design an optimal nano-dimensional channel of fin field effect transistor (FinFET) on the basis of electrical characteristics and constituent semiconductor materials (Si, GaAs, Ge, and InAs) to overcome issues regarding the shrinking of dimensions and ensure the best performance of FinFETs. This objective has been achieved by proposing a new scaling factor, K, to simultaneously shrink the physical scaling limits of channel dimensions for various FinFETs without degrading their performance. A simulation-based comprehensive comparative study depending on four variable parameters (length, width, oxide thickness of the channel, and scaling factor) was carried out. The influence of changing channel dimensions on the performance of each type of FinFET was evaluated according to four electrical characteristics: i) ON-state/OFF-state current (ION/IOFF) ratio, ii) subthreshold swing (SS), iii) threshold voltage, and iv) drain-induced barrier lowering. The well-known multi-gate field-effect transistor (MuGFET) simulation tool for nanoscale MuGFET structure was utilized to conduct experimental simulations under the considered conditions. The obtained simulation results showed that the optimal channel dimensions for the best performance of all considered FinFET types were achieved at a minimal scaling factor K=0.125 with 5 nm length, 2.5 nm width, and 0.625 nm oxide thickness of the channel.
Volume: 20
Issue: 1
Page: 221-234
Publish at: 2022-02-01

Epileptic seizure classification of electroencephalogram signals using extreme gradient boosting classifier

10.11591/ijeecs.v25.i2.pp884-891
Millee Panigrahi , Dayal Kumar Behera , Krishna Chandra Patra
Epilepsy causes repeated seizures in an individual's life, which causes transient irregularities in the brain's electrical activity. It results in different physical symptoms that are abnormal. Various antiepileptic drugs fail to minimize repeated patient seizures. The electroencephalogram (EEG) signal recordings provide us with time-series data set for epileptic seizure detection and analysis. These signals are highly nonlinear and inconsistent, and they are recorded over time. Predicting the ictal period (seizure period at the time of epilepsy) is thus a challenging task in the naked eye for the medical practitioners. Various machine learning techniques are applied to identify the seizure's occurrence and its classification in multiple domains. A classification model based on extreme gradient boosting (SCLXGB) is proposed here for the classification of the EEG signals. The SCLXGB model implements binary seizure classification on the benchmark dataset. Compared with K-nearest neighbor, linear regression, and Decision treebased models, the proposed model achieves the best area under receiver operating curve (AUC) of 0.9462 and an accuracy of 96% which signifies accurate prediction of seizure and non seizure period. The proposed model SCLXGB was validated by taking different performance metrics to indicate the occurrence and non-occurrence of seizures in patients more appropriately.
Volume: 25
Issue: 2
Page: 884-891
Publish at: 2022-02-01

Modified T-type topology of three-phase multi-level inverter for photovoltaic systems

10.11591/ijece.v12i1.pp262-268
Abderrahmane Ouchatti , Redouane Majdoul , Ahmed Moutabir , Abderrahim Taouni , Abdelouahed Touati
In this article, a three-phase multilevel neutral-point-clamped inverter with a modified t-type structure of switches is proposed. A pulse width modulation (PWM) scheme of the proposed inverter is also developed. The proposed topology of the multilevel inverter has the advantage of being simple, on the one hand since it does contain only semiconductors in reduced number (corresponding to the number of required voltage levels), and no other components such as switching or flying capacitors, and on the other hand, the control scheme is much simpler and more suitable for variable frequency and voltage control. The performances of this inverter are analyzed through simulations carried out in the MATLAB/Simulink environment on a three-phase inverter with 9 levels. In all simulations, the proposed topology is connected with R-load or RL-load without any output filter.
Volume: 12
Issue: 1
Page: 262-268
Publish at: 2022-02-01

Comparative analysis of various machine learning algorithms for ransomware detection

10.12928/telkomnika.v20i1.18812
Ban Mohammed; Al-Nahrain University Khammas
Recently, the ransomware attack posed a serious threat that targets a wide range of organizations and individuals for financial gain. So, there is a real need to initiate more innovative methods that are capable of proactively detect and prevent this type of attack. Multiple approaches were innovated to detect attacks using different techniques. One of these techniques is machine learning techniques which provide reasonable results, in most attack detection systems. In the current article, different machine learning techniques are tested to analyze its ability in a detection ransomware attack. The top 1000 features extracted from raw byte with the use of gain ratio as a feature selection method. Three different classifiers (decision tree (J48), random forest, radial basis function (RBF) network) available in Waikato Environment for Knowledge Analysis (WEKA) based machine learning tool are evaluated to achieve significant detection accuracy of ransomware. The result shows that random forest gave the best detection accuracy almost around 98%.
Volume: 20
Issue: 1
Page: 43-51
Publish at: 2022-02-01

Dynamic hand gesture recognition of Arabic sign language by using deep convolutional neural networks

10.11591/ijeecs.v25.i2.pp952-962
Mohammad H. Ismail , Shefa A. Dawwd , Fakhradeen H. Ali
In computer vision, one of the most difficult problems is human gestures in videos recognition Because of certain irrelevant environmental variables. This issue has been solved by using single deep networks to learn spatiotemporal characteristics from video data, and this approach is still insufficient to handle both problems at the same time. As a result, the researchers fused various models to allow for the effective collection of important shape information as well as precise spatiotemporal variation of gestures. In this study, we collected the dynamic dataset for twenty meaningful words of Arabic sign language (ArSL) using a Microsoft Kinect v2 camera. The recorded data included 7350 red, green, and blue (RGB) videos and 7350 depth videos. We proposed four deep neural networks models using 2D and 3D convolutional neural network (CNN) to cover all feature extraction methods and then passing these features to the recurrent neural network (RNN) for sequence classification. Long short-term memory (LSTM) and gated recurrent unit (GRU) are two types of using RNN. Also, the research included evaluation fusion techniques for several types of multiple models. The experiment results show the best multi-model for the dynamic dataset of the ArSL recognition achieved 100% accuracy.
Volume: 25
Issue: 2
Page: 952-962
Publish at: 2022-02-01

Solution for intra/inter-cluster event-reporting problem in cluster-based protocols for wireless sensor networks

10.11591/ijece.v12i1.pp868-879
Raed Taleb Al-Zubi , Abdulraheem Ahmed Kreishan , Mohammad Qasem Alawad , Khalid Ahmad Darabkh
In recent years, wireless sensor networks (WSNs) have been considered one of the important topics for researchers due to their wide applications in our life. Several researches have been conducted to improve WSNs performance and solve their issues. One of these issues is the energy limitation in WSNs since the source of energy in most WSNs is the battery. Accordingly, various protocols and techniques have been proposed with the intention of reducing power consumption of WSNs and lengthen their lifetime. Cluster-oriented routing protocols are one of the most effective categories of these protocols. In this article, we consider a major issue affecting the performance of this category of protocols, which we call the intra/inter-cluster event-reporting problem (IICERP). We demonstrate that IICERP severely reduces the performance of a cluster-oriented routing protocol, so we suggest an effective Solution for IICERP (SIICERP). To assess SIICERP’s performance, comprehensive simulations were performed to demonstrate the performance of several cluster-oriented protocols without and with SIICERP. Simulation results revealed that SIICERP substantially increases the performance of cluster-oriented routing protocols.
Volume: 12
Issue: 1
Page: 868-879
Publish at: 2022-02-01

Comparative analysis of Dimensions and Scopus bibliographic data sources: an approach to university research productivity

10.11591/ijece.v12i1.pp706-720
Pachisa Kulkanjanapiban , Tipawan Silwattananusarn
This paper shows a significant comparison of two primary bibliographic data sources at the document level of Scopus and Dimensions. The emphasis is on the differences in their document coverage by institution level of aggregation. The main objective is to assess whether Dimensions offers at the institutional level good new possibilities for bibliometric analysis as at the global level. The results of a comparative study of the citation count profiles of articles published by faculty members of Prince of Songkla University (PSU) in Dimensions and Scopus from the year the databases first included PSU-authored papers (1970 and 1978, respectively) through the end of June 2020. Descriptive statistics and correlation analysis of 19,846 articles indexed in Dimensions and 13,577 indexed in Scopus. The main finding was that the number of citations received by Dimensions was highly correlated with citation counts in Scopus. Spearman’s correlation between citation counts in Dimensions and Scopus was a high and mighty relationship. The findings mainly affect Dimensions’ possibilities as instruments for carrying out bibliometric analysis of university members’ research productivity. University researchers can use Dimensions to retrieve information, and the design policies can be used to evaluate research using scientific databases.
Volume: 12
Issue: 1
Page: 706-720
Publish at: 2022-02-01

Power quality event classification using complex wavelets phasor models and customized convolution neural network

10.11591/ijece.v12i1.pp22-31
Likhitha Ramalingappa , Aswathnarayan Manjunatha
Origin and triggers of power quality (PQ) events must be identified in prior, in order to take preventive steps to enhance power quality. However it is important to identify, localize and classify the PQ events to determine the causes and origins of PQ disturbances. In this paper a novel algorithm is presented to classify voltage variations into six different PQ events considering the space phasor model (SPM) diagrams, dual tree complex wavelet transforms (DTCWT) sub bands and the convolution neural network (CNN) model. The input voltage data is converted into SPM data, the SPM data is transformed using 2D DTCWT into low pass and high pass sub bands which are simultaneously processed by the 2D CNN model to perform classification of PQ events. In the proposed method CNN model based on Google Net is trained to perform classification of PQ events with default configuration as in deep neural network designer in MATLAB environment. The proposed algorithm achieve higher accuracy with reduced training time in classification of events than compared with reported PQ event classification methods.
Volume: 12
Issue: 1
Page: 22-31
Publish at: 2022-02-01

Efficient organization of nodes in wireless sensor networks (clustering location-based LEACH)

10.11591/ijece.v12i1.pp1011-1017
Mohammed Réda El Ouadi , Abderrahim Hasbi
The rapid development of connected devices and wireless communication has enabled several researchers to study wireless sensor networks and propose methods and algorithms to improve their performance. Wireless sensor networks (WSN) are composed of several sensor nodes deployed to collect and transfer data to base station (BS). Sensor node is considered as the main element in this field, characterized by minimal capacities of storage, energy, and computing. In consequence of the important impact of the energy on network lifetime, several researches are interested to propose different mechanisms to minimize energy consumption. In this work, we propose a new enhancement of low-energy adaptive clustering hierarchy (LEACH) protocol, named clustering location-based LEACH (CLOC-LEACH), which represents a continuity of our previous published work location-based LEACH (LOC-LEACH). The proposed protocol organizes sensor nodes into four regions, using clustering mechanism. In addition, an efficient concept is adopted to choose cluster head. CLOC-LEACH considers the energy as the principal metric to choose cluster heads and uses a gateway node to ensure the inter-cluster communication. The simulation with MATLAB shows that our contribution offers better performance than LEACH and LOC-LEACH, in terms of stability, energy consumption and network lifetime.
Volume: 12
Issue: 1
Page: 1011-1017
Publish at: 2022-02-01

Investigation of temperature gradient between ambient air and soil to power up wireless sensor network device using a thermoelectric generator

10.12928/telkomnika.v20i1.22463
Khalil Azha Mohd; Universiti Teknikal Malaysia Melaka Annuar , Ramizi; Universiti Kebangsaan Malaysia Mohamed , Yushaizad; Universiti Kebangsaan Malaysia Yusof
This paper proposes a study of an energy harvesting system for powering wireless sensor network (WSN) devices. The thermal energy harvesting system used is based on the thermal energy source between ambient air at the soil surface with five depth levels. Measurement was taken for 46 days in a garden area located in Melaka, Malaysia. A feasibility study of soil temperature measurement to obtain a temperature gradient can be used for harvesting by using thermoelectric generators (TEG) modules. Then, the efficiency of TEG with several different configurations based on temperature gradient data has been tested in the laboratory. The results revealed that the depth of soil 6 cm between sensors 1 and 3 will gave the best representation of level average temperature different around 1 ℃. Based on the temperature gradient data, the combination of three TEG SP1848 in a series connection with DC-DC step-up circuit DC1664 will produce an optimum voltage output of about 3 V. This output voltage is enough to operate low power IoT device derived from thermal energy.
Volume: 20
Issue: 1
Page: 185-193
Publish at: 2022-02-01

Internet of things and multi-class deep feature-fusion based classification of tomato leaf disease

10.11591/ijeecs.v25.i2.pp995-1002
Rina mahakud , Binod Kumar Pattanayak , Bibudhendu Pati
A deep transfer learning (deep-TL) classification model has been proposed to diagnose tomato leaf disease. The main challenge of inaccurate classification of a convolution neural network (CNN) model was the availability of the small-sized dataset. This model deals with the challenges like availability of small-sized and imbalanced datasets. The proposed Alex support vector machine (SVM) fused hybrid classification (ASFHC) model is based on fully fusion technology that avoids overfitting to classify the type of disease in tomato leaves. The proposed model achieves the best performance in terms of accuracy by data augmentation of the training data. It uses a pre-trained network for feature extraction with the modification of architecture by concatenating two layers FC6 and FC7 (fully connected layer), plus a linear SVM classifier for classification of the disease. The uniqueness of the research is although the dataset is not balanced, the performance of the model has achieved the maximum. Compared with VGG 16 and VGG 19, the proposed model (ASFHC) has been evaluated using different measuring parameters, indicating remarkable computation time for implementation in the internet of things (IoT) domain. The overall accuracy attained by the model is 99.62%.
Volume: 25
Issue: 2
Page: 995-1002
Publish at: 2022-02-01

Performance evaluation of ad-hoc on-demand distance vector protocol in highway environment in VANET with MATLAB

10.12928/telkomnika.v20i1.20876
Osama A.; Northern Technical University Qasim , Mohammed Sami; Northern Technical University Noori , Mohand Lokman; Northern Technical University Ahmad Al Dabag
Vehicular ad-hoc network (VANET), the development of this network in recent years has become one of the most important areas of research. The primary goal of using the VANET network is to reduce the number of deaths and enhance road safety. VANET network faces some problems when routing packets between vehicles, due to the high-speed movement of vehicles. Therefore, researchers have begun to develop routing protocols in the VANET network to overcome these problems when routing packets between vehicles. In this study, the effect of changing the number of vehicles on the performance of ad-hoc on-demand distance vector (AODV) protocol will be studied in the highway environment and in the case of vehicle movement at variable speeds between (40-120 km/h) and the simulation time is 200 sec. The ad-hoc ondemand distance vector protocol performance was evaluated by three performance measures (end-to-end delay, dropped packets, overhead and packet delivery ratio).
Volume: 20
Issue: 1
Page: 194-200
Publish at: 2022-02-01

Design and development of DrawBot using image processing

10.11591/ijece.v12i1.pp365-375
Krithika Vaidyanathan , Nandhini Murugan , Subramani Chinnamuthu , Sivashanmugam Shivasubramanian , Surya Raghavendran , Vimala Chinnaiyan
Extracting text from an image and reproducing them can often be a laborious task. We took it upon ourselves to solve the problem. Our work is aimed at designing a robot which can perceive an image shown to it and reproduce it on any given area as directed. It does so by first taking an input image and performing image processing operations on the image to improve its readability. Then the text in the image is recognized by the program. Points for each letter are taken, then inverse kinematics is done for each point with MATLAB/Simulink and the angles in which the servo motors should be moved are found out and stored in the Arduino. Using these angles, the control algorithm is generated in the Arduino and the letters are drawn.
Volume: 12
Issue: 1
Page: 365-375
Publish at: 2022-02-01

Characteristic's analysis of associative switching system

10.12928/telkomnika.v20i1.17640
Svetlana A; Tashkent University of Information Technologies, Republic of Uzbekistan Sadchikova , Mubarak; Tashkent University of Information Technologies, Republic of Uzbekistan Abdujapparova
This paper introduced new method and model of telecommunication switching system design which can be applied to wavelength-division multiplexing (WDM) optical networks, circuit-switching networks or virtual channel/path connections in an asynchronous transfer mode (ATM) networks. Modern data switching systems such as electronic private branch exchange (PBX), routers and switches include switching matrix which are constructed in the form of bipartite graphs. In such systems, the issues of requests’ processing are considered from the queuing theory point of view. Associative switching systems are fundamentally new structures, therefore it is necessary to develop adequate methods for their throughput determination. Article covered matters of throughput determination basics of an associative switching system and the obtained formulas used for state probability calculation of switching modules and system throughput.
Volume: 20
Issue: 1
Page: 27-33
Publish at: 2022-02-01
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