• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.55-64
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• 2023

The Internet of Things (IoT) is the combination of the internet and various sensing devices. IoT security has increasingly attracted extensive attention. However, significant losses appears due to malicious attacks. Therefore, intrusion detection, which detects malicious attacks and their behaviors in IoT devices plays a crucial role in IoT security. The intrusion detection system, namely IDS should be executed efficiently by conducting classification and efficient feature extraction techniques. To effectively perform Intrusion detection in IoT applications, a novel method based on a Conventional Neural Network (CNN) for classification and an improved Genetic Algorithm (GA) for extraction is proposed and implemented. Existing issues like failing to detect the few attacks from smaller samples are focused, and hence the proposed novel CNN is applied to detect almost all attacks from small to large samples. For that purpose, the feature selection is essential. Thus, the genetic algorithm is improved to identify the best fitness values to perform accurate feature selection. To evaluate the performance, the NSL-KDDCUP dataset is used, and two datasets such as KDDTEST²¹ and KDDTEST⁺ are chosen. The performance and results are compared and analyzed with other existing models. The experimental results show that the proposed algorithm has superior intrusion detection rates to existing models, where the accuracy and true positive rate improve and the false positive rate decrease. In addition, the proposed algorithm indicates better performance on KDDTEST⁺ than KDDTEST²¹ because there are few attacks from minor samples in KDDTEST⁺. Therefore, the results demonstrate that the novel proposed CNN with the improved GA can identify almost every intrusion.

• Journal of Electrical Engineering and Technology
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• v.4 no.4
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• pp.559-565
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• 2009

Power dissipation during scan testing is becoming an important concern as design sizes and gate densities increase. The high switching activity of combinational circuits is an unnecessary operation in scan shift mode. In this paper, we present a novel architecture to reduce test power dissipation in combinational logic by blocking signal transitions at the logic inputs during scan shifting. We propose a unique architecture that uses dmuxed scan flip-flop (DSF) and transmission gate as an alternative to muxed scan flip-flop. The proposed method does not have problems with auto test pattern generation (ATPG) techniques such as test application time and computational complexity. Moreover, our elegant method improves performance degradation and large overhead in terms of area with blocking logic techniques. Experimental results on ITC99 benchmarks show that the proposed architecture can achieve an average improvement of 30.31% in switching activity compared to conventional scan methods. Additionally, the results of simulation with DSF indicate that the powerdelay product (PDP) and area overhead are improved by 28.9% and 15.6%, respectively, compared to existing blocking logic method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.2
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• pp.337-343
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• 2012

The techniques of implementing 3D movie have been developed by stereoscopic representation methods of the scene based on human visual experience. Recently, though various novel approaches for stereo movies are proposed to produce realistic 3D image, more study have to be done for compensating keystone distortion which is generated by moving virtual camera. In this paper we propose a novel production technique which minimizes keystone distortion based on analyzing pixel distance, and is easily implemented on popular graphics environment. First, in graphics environment we categorize each objects as individual layers, and extract image data to produce 3D image. The comparison between each animation sequences from proposed and conventional production methods shows that our production technique well compensate the distortion.

• Journal of the Korean Society for Nondestructive Testing
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• v.28 no.1
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• pp.46-53
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• 2008

Ultrasonic C-scan technique is one of very popular techniques being used for detection of flaws in polymer matrix composite(PMC). However, the application of this technique is very limited for evaluation of defects in PMC fabricated by the automated fiber placement process. The purpose of this study is to develop a novel ultrasonic hybrid system based on nondestructive and non-contact ultrasonic techniques for evaluation of delamination in carbon/epoxy and carbon/PPS composite laminates. It was shown that the newly developed ultrasonic hybrid system based on dual air-coupled pitch-catch technique with ultrasonic scattering reflection concept could provide excellent image with higher resolution of delamination in PMC compared with the conventional pitch-catch method. It is expected that this ultrasonic hybrid technique can be applied for on-line inspection of flaws in PMC during the fabrication process.

• Radiation Oncology Journal
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• v.35 no.1
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• pp.32-38
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• 2017

Purpose: Several accelerated partial breast radiation (APBR) techniques have been investigated in patients with early-stage breast cancer (BC); however, the optimal treatment delivery techniques remain unclear. We evaluated the feasibility and toxicity of APBR delivered using intensity-modulated radiation therapy (IMRT) in elderly patients with stage I BC, using a novel fractionation schedule. Materials and Methods: Forty-two patients aged ${\geq}65$ years, with stage I BC who underwent breast conserving surgery were enrolled in a phase I/II study evaluating APBR using IMRT. Forty eligible patients received 40 Gy in 4 Gy daily fractions. Patients were assessed for treatment related toxicities, and cosmesis, before APBR, during, and after completion of the treatment. Results: The median age was 73 years, median tumor size 0.8 cm and the median follow-up was 54 months. The 5-year locoregional control was 97.5% and overall survival 90%. Erythema and skin pigmentation was the most common acute adverse event, reported by 27 patients (69%). Twenty-six patients (65%) reported mild pain, rated 1-4/10. This improved at last follow-up to only 2 (15%). Overall the patient and physician reported worst late toxicities were lower than the baseline and at last follow-up, patients and physicians rated cosmesis as excellent/good in 93% and 86 %, respectively. Conclusion: In this prospective trial, we observed an excellent rate of tumor control with daily APBR. The acceptable toxicity profile and cosmetic results of this study support the use of IMRT planned APBR with daily schedule in elderly patients with early stage BC.

• Smart Structures and Systems
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• v.6 no.3
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• pp.225-238
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• 2010

We report on the development of a new technology for the fabrication of Micro-Electro-Mechanical-System (MEMS) strain sensors to realize a novel type of crackmeter for health monitoring of ageing civil infrastructures. The fabrication of micromachined silicon MEMS sensors based on a Silicon On Insulator (SOI) technology, designed according to a Double Ended Tuning Fork (DETF) geometry is presented, using a novel process which includes a gap narrowing procedure suitable to fabricate sensors with low motional resistance. In order to employ these sensors for crack monitoring, techniques suited for bonding the MEMS sensors on a steel surface ensuring good strain transfer from steel to silicon and a packaging technique for the bonded sensors are proposed, conceived for realizing a low-power crackmeter for ageing infrastructure monitoring. Moreover, the design of a possible crackmeter geometry suited for detection of crack contraction and expansion with a resolution of $10{\mu}m$ and very low power consumption requirements (potentially suitable for wireless operation) is presented. In these sensors, the small crackmeter range for the first field use is related to long-term observation on existing cracks in underground tunnel test sections.

• The Journal of Advanced Prosthodontics
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• v.11 no.3
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• pp.179-186
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• 2019

PURPOSE. The purpose of this study was to compare two novel impression methods and a conventional impression method for edentulous jaws using 3-dimensional (3D) analysis software. MATERIALS AND METHODS. Five edentulous patients (four men and one woman; mean age: 62.7 years) were included. Three impression techniques were used: conventional impression method (CI; control), simple modified closed-mouth impression method with a novel tray (SI), and digital impression method using an intraoral scanner (DI). Subsequently, a gypsum model was made, scanned, and superimposed using 3D analysis software. Mean area displacement was measured using CI method to evaluate differences in the impression surfaces as compared to those values obtained using SI and DI methods. The values were confirmed at two to five areas to determine the differences. CI and SI were compared at all areas, while CI and DI were compared at the supporting areas. Kruskal-Wallis test was performed for all data. Statistical significance was considered at P value <.05. RESULTS. In the comparison of the CI and SI methods, the greatest difference was observed in the mandibular vestibule without statistical significance (P>.05); the difference was < 0.14 mm in the maxilla. The difference in the edentulous supporting areas between the CI and DI methods was not significant (P>.05). CONCLUSION. The CI, SI, and DI methods were effective in making impressions of the supporting areas in edentulous patients. The SI method showed clinically applicability.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.143-151
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• 2021

The enormous prevalence of transferring official confidential digital documents via the Internet shows the urgent need to deliver confidential messages to the recipient without letting any unauthorized person to know contents of the secret messages or detect there existence . Several Steganography techniques such as the least significant Bit (LSB), Secure Cover Selection (SCS), Discrete Cosine Transform (DCT) and Palette Based (PB) were applied to prevent any intruder from analyzing and getting the secret transferred message. The utilized steganography methods should defiance the challenges of Steganalysis techniques in term of analysis and detection. This paper presents a novel and robust framework for color image steganography that combines Linear Congruential Generator (LCG), simulated annealing (SA), Cesar cryptography and LSB substitution method in one system in order to reduce the objection of Steganalysis and deliver data securely to their destination. SA with the support of LCG finds out the optimal minimum sniffing path inside a cover color image (RGB) then the confidential message will be encrypt and embedded within the RGB image path as a host medium by using Cesar and LSB procedures. Embedding and extraction processes of secret message require a common knowledge between sender and receiver; that knowledge are represented by SA initialization parameters, LCG seed, Cesar key agreement and secret message length. Steganalysis intruder will not understand or detect the secret message inside the host image without the correct knowledge about the manipulation process. The constructed system satisfies the main requirements of image steganography in term of robustness against confidential message extraction, high quality visual appearance, little mean square error (MSE) and high peak signal noise ratio (PSNR).

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.230-240
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• 2022

Sharing of online videos via internet is an emerging and important concept in different types of applications like surveillance and video mobile search in different web related applications. So there is need to manage personalized web video retrieval system necessary to explore relevant videos and it helps to peoples who are searching for efficient video relates to specific big data content. To evaluate this process, attributes/features with reduction of dimensionality are computed from videos to explore discriminative aspects of scene in video based on shape, histogram, and texture, annotation of object, co-ordination, color and contour data. Dimensionality reduction is mainly depends on extraction of feature and selection of feature in multi labeled data retrieval from multimedia related data. Many of the researchers are implemented different techniques/approaches to reduce dimensionality based on visual features of video data. But all the techniques have disadvantages and advantages in reduction of dimensionality with advanced features in video retrieval. In this research, we present a Novel Intent based Dimension Reduction Semi-Supervised Learning Approach (NIDRSLA) that examine the reduction of dimensionality with explore exact and fast video retrieval based on different visual features. For dimensionality reduction, NIDRSLA learns the matrix of projection by increasing the dependence between enlarged data and projected space features. Proposed approach also addressed the aforementioned issue (i.e. Segmentation of video with frame selection using low level features and high level features) with efficient object annotation for video representation. Experiments performed on synthetic data set, it demonstrate the efficiency of proposed approach with traditional state-of-the-art video retrieval methodologies.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.131-138
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• 2022

Novel Coronavirus (COVID-19) is viewed as one of the main general wellbeing theaters on the worldwide level all over the planet. Because of the abrupt idea of the flare-up and the irresistible force of the infection, it causes individuals tension, melancholy, and other pressure responses. The avoidance and control of the novel Covid pneumonia have moved into an imperative stage. It is fundamental to early foresee and figure of infection episode during this troublesome opportunity to control of its grimness and mortality. The entire world is investing unimaginable amounts of energy to fight against the spread of this lethal infection. In this paper, we utilized machine learning and deep learning techniques for analyzing what is going on utilizing countries shared information and for detecting the climate factors that effect on spreading Covid-19, such as humidity, sunny hours, temperature and wind speed for understanding its regular dramatic way of behaving alongside the forecast of future reachability of the COVID-2019 around the world. We utilized data collected and produced by Kaggle and the Johns Hopkins Center for Systems Science. The dataset has 25 attributes and 9566 objects. Our Experiment consists of two phases. In phase one, we preprocessed dataset for DL model and features were decreased to four features humidity, sunny hours, temperature and wind speed by utilized the Pearson Correlation Coefficient technique (correlation attributes feature selection). In phase two, we utilized the traditional famous six machine learning techniques for numerical datasets, and Dense Net deep learning model to predict and detect the climatic factor that aide to disease outbreak. We validated the model by using confusion matrix (CM) and measured the performance by four different metrics: accuracy, f-measure, recall, and precision.