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  • 1
    Online Resource
    Online Resource
    Comparison hybrid techniques-based mixed transform using compression and quality metrics
    Institute of Advanced Engineering and Science ; 2023
    In:  Indonesian Journal of Electrical Engineering and Computer Science Vol. 30, No. 2 ( 2023-05-01), p. 807-
    Details
    In: Indonesian Journal of Electrical Engineering and Computer Science, Institute of Advanced Engineering and Science, Vol. 30, No. 2 ( 2023-05-01), p. 807-
    Abstract: Image quality plays a vital role in improving and assessing image compression performance. Image compression represents big image data to a new image with a smaller size suitable for storage and transmission. This paper aims to evaluate the implementation of the hybrid techniques-based tensor product mixed transform. Compression and quality metrics such as compression-ratio (CR), rate-distortion (RD), peak signal-to-noise ratio (PSNR), and Structural Content (SC) are utilized for evaluating the hybrid techniques. Then, a comparison between techniques is achieved according to these metrics to estimate the best technique. The main contribution is to improve the hybrid techniques. The proposed hybrid techniques are consisting of discrete wavelet transform (W), multi-wavelet transform (M), and tensor product mixed transform (T) as 1-level W, M, and T techniques. WT and MT are the 2-level techniques, while WWT, WMT, MWT, and MMT are the 3-level techniques. For each level of each technique, a reconstructed process is applied. The simulation results using MATLAB 2019a indicated that the MMT is the best technique with CR=1024, R(D)=4.154, and PSNR=81.9085. Also, it is faster than the other techniques in the previous works as compared with them. Further research might investigate whether this technique can benefit image and speech recognition.
    Type of Medium: Online Resource
    ISSN: 2502-4760 , 2502-4752
    URL: Issue
    URL: Article
    DOI: 10.11591/ijeecs.v30.i2
    DOI: 10.11591/ijeecs.v30.i2.pp807-816
    Language: Unknown
    Publisher: Institute of Advanced Engineering and Science
    Publication Date: 2023
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  • 2
    Online Resource
    Online Resource
    A review on various security attacks in vehicular ad hoc networks
    Institute of Advanced Engineering and Science ; 2021
    In:  Bulletin of Electrical Engineering and Informatics Vol. 10, No. 5 ( 2021-10-01), p. 2627-2635
    Details
    In: Bulletin of Electrical Engineering and Informatics, Institute of Advanced Engineering and Science, Vol. 10, No. 5 ( 2021-10-01), p. 2627-2635
    Abstract: Ad hoc vehicle networks (VANET) are being established as a primary form of mobile ad hoc networks (MANET) and a critical infrastructure to provide vehicle passengers with a wide range of safety applications. VANETs are increasingly common nowadays because it is connecting to a wide range of invisible services. The security of VANETs is paramount as their future use must not jeopardize their users' safety and privacy. The security of these VANETs is essential for the benefit of secure and effective security solutions and facilities, and uncertainty remains, and research in this field remains fast increasing. We discussed the challenges in VANET in this survey. Were vehicles and communication in VANET are efficient to ensure communication between vehicles to vehicles (V2V), vehicles to infrastructures (V2I). Clarified security concerns have been discussed, including confidentiality, authentication, integrity, availableness, and non-repudiation. We have also discussed the potential attacks on security services. According to analysis and performance evaluations, this paper shows that the ACPN is both feasible and appropriate for effective authentication in the VANET. Finally, the article found that in VANETs, encryption and authentication are critical.
    Type of Medium: Online Resource
    ISSN: 2302-9285 , 2089-3191
    URL: Issue
    URL: Article
    DOI: 10.11591/eei.v10i5
    DOI: 10.11591/eei.v10i5.3127
    Language: Unknown
    Publisher: Institute of Advanced Engineering and Science
    Publication Date: 2021
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  • 3
    Online Resource
    Online Resource
    Provably secure and efficient audio compression based on compressive sensing
    Institute of Advanced Engineering and Science ; 2023
    In:  International Journal of Electrical and Computer Engineering (IJECE) Vol. 13, No. 1 ( 2023-02-01), p. 335-
    Details
    In: International Journal of Electrical and Computer Engineering (IJECE), Institute of Advanced Engineering and Science, Vol. 13, No. 1 ( 2023-02-01), p. 335-
    Abstract: 〈 span lang="EN-US" 〉 The advancement of systems with the capacity to compress audio signals and simultaneously secure is a highly attractive research subject. This is because of the need to enhance storage usage and speed up the transmission of data, as well as securing the transmission of sensitive signals over limited and insecure communication channels. Thus, many researchers have studied and produced different systems, either to compress or encrypt audio data using different algorithms and methods, all of which suffer from certain issues including high time consumption or complex calculations. This paper proposes a compressing sensing-based system that compresses audio signals and simultaneously provides an encryption system. The audio signal is segmented into small matrices of samples and then multiplied by a non-square sensing matrix generated by a Gaussian random generator. The reconstruction process is carried out by solving a linear system using the pseudoinverse of Moore-Penrose. The statistical analysis results obtaining from implementing different types and sizes of audio signals prove that the proposed system succeeds in compressing the audio signals with a ratio reaching 28% of real size and reconstructing the signal with a correlation metric between 0.98 and 0.99. It also scores very good results in the normalized mean square error (MSE), peak signal-to-noise ratio metrics (PSNR), and the structural similarity index (SSIM), as well as giving the signal a high level of security. 〈 /span 〉
    Type of Medium: Online Resource
    ISSN: 2722-2578 , 2088-8708
    URL: Issue
    URL: Article
    DOI: 10.11591/ijece.v13i1
    DOI: 10.11591/ijece.v13i1.pp335-346
    Language: Unknown
    Publisher: Institute of Advanced Engineering and Science
    Publication Date: 2023
    detail.hit.zdb_id: 2667127-X
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  • 4
    Online Resource
    Online Resource
    Securing audio transmission based on encoding and steganography
    Institute of Advanced Engineering and Science ; 2021
    In:  Indonesian Journal of Electrical Engineering and Computer Science Vol. 22, No. 3 ( 2021-06-01), p. 1777-
    Details
    In: Indonesian Journal of Electrical Engineering and Computer Science, Institute of Advanced Engineering and Science, Vol. 22, No. 3 ( 2021-06-01), p. 1777-
    Abstract: One of the things that must be considered when establishing a data exchange connection is to make that communication confidential and hide the file’s features when the snoopers intercept it. In this work, transformation (encoding) and steganography techniques are invested to produce an efficient system to secure communication for an audio signal by producing an efficient method to transform the signal into a red–green–blue (RGB) image. Subsequently, this image is hidden in a cover audio file by using the least significant bit (LSB) method in the spatial and transform domains using discrete wavelet transform. The audio files of the message and the cover are in *.wav format. The experimental results showed the success of the transformation in concealing audio secret messages, as well the remarkability of the stego signal quality in both techniques. A peak signal-to-noise ratio peak signal-to-noise ratio (PSNR) scored (20-26) dB with wavelet and (81-112) dB with LSB for cover file size 4.96 MB and structural similarity index metric structural similarity index metric (SSIM) has been used to measure the signal quality which gave 1 with LSB while wavelet was (0.9-1), which is satisfactory in all experimented signals with low time consumption. This work also used these metrics to compare the implementation of LSB and WAV.
    Type of Medium: Online Resource
    ISSN: 2502-4760 , 2502-4752
    URL: Issue
    URL: Article
    DOI: 10.11591/ijeecs.v22.i3
    DOI: 10.11591/ijeecs.v22.i3.pp1777-1786
    Language: Unknown
    Publisher: Institute of Advanced Engineering and Science
    Publication Date: 2021
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  • 5
    Online Resource
    Online Resource
    Audio steganography with enhanced LSB method for securing encrypted text with bit cycling
    Institute of Advanced Engineering and Science ; 2022
    In:  Bulletin of Electrical Engineering and Informatics Vol. 11, No. 1 ( 2022-02-01), p. 185-194
    Details
    In: Bulletin of Electrical Engineering and Informatics, Institute of Advanced Engineering and Science, Vol. 11, No. 1 ( 2022-02-01), p. 185-194
    Abstract: Data security pressing issue, particularly in terms of ensuring secure and reliable data transfer over a network. Encryption and seganography play a fundamental role in the task of securing data exchanging. In this article, both steganography and cryptography were combined to produce a powerful hybrid securing stego-system. Firstly, a text message is encrypted with a new method using a bits cycling operation to give a cipher text. In the second stage, an enhanced LSB method is used to hide the text bits randomly in an audio file of a wav format. This hybrid method can provide effectually secure data. Peak signal-to-noise ratio (PSNR), mean squared error (MSE) and structural similarity (SSIM) were employed to evaluate the performance of the proposed system. A PSNR was in range (60-65) dB with the enhanced least significant bit (LSB) and the SSIM had been invested to calculate the signal quality, which scored 0.999. The experimental results demonstrated that our algorithm is highly effective in securing data and the capacity size of the secured text. Furthermore, the time consumption was considerably low, at less than 0.3 seconds.
    Type of Medium: Online Resource
    ISSN: 2302-9285 , 2089-3191
    URL: Issue
    URL: Article
    DOI: 10.11591/eei.v11i1
    DOI: 10.11591/eei.v11i1.3279
    Language: Unknown
    Publisher: Institute of Advanced Engineering and Science
    Publication Date: 2022
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  • 6
    Online Resource
    Online Resource
    Lifetime enhancement for clustering protocols in heterogeneous wireless sensor networks
    Institute of Advanced Engineering and Science ; 2019
    In:  Indonesian Journal of Electrical Engineering and Computer Science Vol. 14, No. 3 ( 2019-06-01), p. 1305-
    Details
    In: Indonesian Journal of Electrical Engineering and Computer Science, Institute of Advanced Engineering and Science, Vol. 14, No. 3 ( 2019-06-01), p. 1305-
    Abstract: 〈 span style="font-family: Arial, sans-serif; font-size: 9pt;" 〉 In this paper, we propose a new clustering method called fuzzy stable election protocol (FSEP), which is capable to overcome the bottleneck problem and addressing the uneven energy consumption problem in heterogeneous WSNs. We also propose an energy-efficient routing method called particle swarm optimization routing method (PSORM) to find the optimal routing path for the heterogeneous WSNs. PSORM seeks to investigate the problems of balancing energy consumption and maximization of network lifetime. To demonstrate the effectiveness of FSEP-PSORM in terms of lessening end-to-end delay, balancing energy consumption, and maximization of heterogeneous network lifetime, we compare our method with three approaches namely, chessboard clustering approach, PEGASIS, and LEACH. Simulation results show that the network lifetime achieved by FSEP-PSORM could be increased by nearly 38%, 45%, and 60% more than that obtained by PEGASIS, LEACH and stable election protocol clustering (SEP), respectively. 〈 /span 〉
    Type of Medium: Online Resource
    ISSN: 2502-4760 , 2502-4752
    URL: Issue
    URL: Article
    DOI: 10.11591/ijeecs.v14.i3
    DOI: 10.11591/ijeecs.v14.i3.pp1305-1314
    Language: Unknown
    Publisher: Institute of Advanced Engineering and Science
    Publication Date: 2019
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  • 7
    Online Resource
    Online Resource
    Human visualization system based intensive contrast improvement of the collected COVID-19 images
    Institute of Advanced Engineering and Science ; 2022
    In:  Indonesian Journal of Electrical Engineering and Computer Science Vol. 27, No. 3 ( 2022-09-01), p. 1502-
    Details
    In: Indonesian Journal of Electrical Engineering and Computer Science, Institute of Advanced Engineering and Science, Vol. 27, No. 3 ( 2022-09-01), p. 1502-
    Abstract: Enhancement  〈 span 〉 and color correction of images play an important role and can be considered as one of the fundamental and basic operations in image analysis for the purpose of spee 〈 a name="_GoBack" 〉 〈 /a 〉 ding up the diagnosis of the medical images. Improving the quality and contrast of the medical image is the basic requirement for clinicians for obtaining an accurate and accurate medical diagnosis. Thus, getting a clear X-ray image reduces the effort and time-wasting. In this study a new idea will be applied for improving image contrast of the collected COVID-19 X-ray images, this idea is based on using Wiener filter, multilevel of histogram equalization (HE) technique with OpenCV library and then using contrast limited adaptive histogram equalization (CLAHE) techniques with OpenCV library. The proposed methodology programmed in MATLAB software and then implemented using Rasperry Pi 3 model B. The size and resolution of images are different as inputted images and this difference succeeded in proving the strength of the proposed idea. The collected X-ray images have undergone experiential evaluations which clearly showed the effective performance of the proposed methodology. 〈 /span 〉
    Type of Medium: Online Resource
    ISSN: 2502-4760 , 2502-4752
    URL: Issue
    URL: Article
    DOI: 10.11591/ijeecs.v27.i3
    DOI: 10.11591/ijeecs.v27.i3.pp1502-1508
    Language: Unknown
    Publisher: Institute of Advanced Engineering and Science
    Publication Date: 2022
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  • 8
    Online Resource
    Online Resource
    Predictive maintenance of electromechanical systems based on enhanced generative adversarial neural network with convolutional neural network
    Institute of Advanced Engineering and Science ; 2023
    In:  IAES International Journal of Artificial Intelligence (IJ-AI) Vol. 12, No. 4 ( 2023-12-01), p. 1704-
    Details
    In: IAES International Journal of Artificial Intelligence (IJ-AI), Institute of Advanced Engineering and Science, Vol. 12, No. 4 ( 2023-12-01), p. 1704-
    Abstract: 〈 p 〉 Predictive maintenance (PdM) is a cost-cutting method that involves avoiding breakdowns and production losses. Deep learning (DL) algorithms can be used for defect prediction and diagnostics due to the huge amount of data generated by the integration of analog and digital systems in manufacturing operations. To improve the predictive maintenance strategy, this study uses a hybrid of the convolutional neural network (CNN) and conditional generative adversarial neural network (CGAN) model. The proposed CNN-CGAN algorithm improves forecast accuracy while substantially reducing model complexity. A comparison with standalone CGAN utilizing a public dataset is performed to evaluate the proposed model. The results show that the proposed CNN-CGAN model outperforms the conditional GAN (CGAN) in terms of prediction accuracy. The average F-Score is increased from 97.625% for the CGAN to 100% for the CNN-CGAN. 〈 /p 〉
    Type of Medium: Online Resource
    ISSN: 2252-8938 , 2089-4872
    URL: Issue
    URL: Article
    DOI: 10.11591/ijai.v12.i4
    DOI: 10.11591/ijai.v12.i4.pp1704-1712
    Language: Unknown
    Publisher: Institute of Advanced Engineering and Science
    Publication Date: 2023
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  • 9
    Online Resource
    Online Resource
    Fully automated model on breast cancer classification using deep learning classifiers
    Institute of Advanced Engineering and Science ; 2022
    In:  Indonesian Journal of Electrical Engineering and Computer Science Vol. 28, No. 1 ( 2022-10-01), p. 183-
    Details
    In: Indonesian Journal of Electrical Engineering and Computer Science, Institute of Advanced Engineering and Science, Vol. 28, No. 1 ( 2022-10-01), p. 183-
    Abstract: Deep learning models on the same database have varied accuracy ratings; as such, additional parameters, such as pre-processing, data augmentation and transfer learning, can influence the models’ capacity to obtain higher accuracy. In this paper, a fully automated model is designed using deep learning algorithm to capture images from patients and pre-process, segment and classify the intensity of cancer spread. In the first pre-processing step, pectoral muscles are removed from the input images, which are then downsized. The removal of pectoral muscles after identification may become crucial in classification systems. Finally, the pectoral musclesaredeleted from the picture by using an area expanding segmentation. All mammograms are downsized to reduce processing time. Each stage of the fully automated model uses an optimisation approach to obtain highaccuracy results at respective stages. Simulation is conducted to test the efficacy of the model against state-of-art models, and the proposed fully automated model is thoroughly investigated. For a more accurate comparison, we include the model in our analysis. In a nutshell, this work offers a wealth of information as well as review and discussion of the experimental conditions used by studies on classifying breast cancer images.
    Type of Medium: Online Resource
    ISSN: 2502-4760 , 2502-4752
    URL: Issue
    URL: Article
    DOI: 10.11591/ijeecs.v28.i1
    DOI: 10.11591/ijeecs.v28.i1.pp183-191
    Language: Unknown
    Publisher: Institute of Advanced Engineering and Science
    Publication Date: 2022
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