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Institution

Chittagong University of Engineering & Technology

EducationChittagong, Bangladesh
About: Chittagong University of Engineering & Technology is a education organization based out in Chittagong, Bangladesh. It is known for research contribution in the topics: Renewable energy & Dielectric. The organization has 1200 authors who have published 1444 publications receiving 10418 citations. The organization is also known as: Engineering College, Chittagong & Bangladesh Institute of Technology, Chittagong.


Papers
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Proceedings ArticleDOI
23 May 2014
TL;DR: A new embedding algorithm (NEA) of digital watermarking is proposed and evaluated by comparing performances with Cox's algorithm, the performances of NEA will compare among other algorithms like Gaussian sequence, image fusion, nonlinear quantization embedding with various attacking conditions in near future.
Abstract: The authenticity of content or matter is crucial factors for solving the problem of copying, modifying, and distributing the intellectual properties in an illegal way. Watermarking can resolve the stealing problem of intellectual properties. This paper considers a robust image watermarking technique based on discrete wavelet transform (WDT) and discrete cosine transform (DCT) called hybrid watermarking. The hybrid watermarking is performed by two level, three level, and four level DWT followed by respective DCT on the host image. A new embedding algorithm (NEA) of digital watermarking is proposed in this paper. The simulation results are compared with Cox's additive embedding algorithm and the NEA for additive white Gaussian noise (AWGN) attack and without attack. Both algorithms use the hybrid watermarking. The NEA gives 3.04dB and 9.33dB better pick signal to noise ratio (PSNR) compared to Cox's additive algorithm for the 4 level DWT for AWGN attack and without attack, respectively. Moreover, the NEA extracts the marked image 46 times better of Cox's additive algorithm in 2 level DWT with AWGN attack. That means, the NEA can embed larger marks and high quality marks extract from the embedded watermarking even attacking condition. Though the NEA is evaluated in this paper by comparing performances with Cox's algorithm, the performances of NEA will compare among other algorithms like Gaussian sequence, image fusion, nonlinear quantization embedding with various attacking conditions in near future.

47 citations

Journal ArticleDOI
TL;DR: The experimental results show that the proposed recency-based approach better predicts individual’s phone usage behavior than existing baseline models, by minimizing the error rate in various context-aware test cases.
Abstract: Due to the advanced features in recent smartphones and context-awareness in mobile technologies, users’ diverse behavioral activities with their phones and associated contexts are recorded through the device logs. Behavioral patterns of smartphone users may vary greatly between individuals in different contexts—for example, temporal, spatial, or social contexts. However, an individual’s phone usage behavior may not be static in the real-world changing over time. The volatility of usage behavior will also vary from user-to-user. Thus, an individual’s recent behavioral patterns and corresponding machine learning rules are more likely to be interesting and significant than older ones for modeling and predicting their phone usage behavior. Based on this concept of recency, in this paper, we present an approach for mining recency-based personalized behavior, and name it “RecencyMiner” for short, utilizing individual’s contextual smartphone data, in order to build a context-aware personalized behavior prediction model. The effectiveness of RecencyMiner is examined by considering individual smartphone user’s real-life contextual datasets. The experimental results show that our proposed recency-based approach better predicts individual’s phone usage behavior than existing baseline models, by minimizing the error rate in various context-aware test cases.

47 citations

Journal ArticleDOI
TL;DR: In this article, the elastic properties, Debye temperature, Mulliken population, Vickers hardness, and charge density of two recently synthesized superconducting ScRhP and ScIrP pnictides are investigated.
Abstract: For the first time, we have reported in this study an ab initio investigation on elastic properties, Debye temperature, Mulliken population, Vickers hardness, and charge density of the two recently synthesized superconducting ScRhP and ScIrP pnictides The optimized cell parameters show fair agreement with the experimental results The mechanical stability of both ternary phosphides is confirmed via the calculated elastic constants Both compounds are ductile in nature and damage tolerant ScIrP is expected to be elastically more anisotropic than ScRhP The estimated value of Debye temperature predicts that ScRhP is thermally more conductive than ScIrP and the phonon frequency in ScRhP is higher than that in ScIrP Both pnictides are soft and easily machinable due to their low Vickers hardness Moreover, the hardness of ScRhP is lower due to the presence of antibonding Rh-Rh in ScRhP The metallic conductivity of ScRhP reduces significantly when Rh is replaced with Ir The main contribution to the total density of states (TDOS) at Fermi-level (EF) comes from d-electrons of Sc and Rh/Ir in both pnictides These two ternary compounds are characterized mainly by metallic and covalent bonding with little ionic contribution The calculated superconducting transition temperatures fairly coincide with the reported measured values

45 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a (G ′/G )-expansion method for finding exact traveling wave solutions of nonlinear partial differential equations (NLEEs) using hyperbolic, trigonometric and rational functions.
Abstract: The novel ( G ′/ G )-expansion method is one of the powerful methods that appeared in recent times for establishing exact traveling wave solutions of nonlinear partial differential equations. Exact traveling wave solutions in terms of hyperbolic, trigonometric and rational functions to the cubic nonlinear Klein–Gordon equation via this method are obtained in this article. The efficiency of this method for finding exact solutions and traveling wave solutions has been demonstrated. It is shown that the novel ( G ′/ G )-expansion method is a simple and valuable mathematical tool for solving nonlinear evolution equations (NLEEs) in applied mathematics, mathematical physics and engineering.

45 citations

Journal ArticleDOI
01 Jul 2020
TL;DR: In this paper, the authors summarized the recent AI and ML-based studies that have addressed the pandemic and identified seven future research directions that would guide researchers to conduct future research, including developing new treatment options, explore the contextual effect and variation in research outcomes, support the health care workforce, and explore the effect of research outcomes based on different types of data.
Abstract: Artificial intelligence (AI) and machine learning (ML) have caused a paradigm shift in healthcare that can be used for decision support and forecasting by exploring medical data. Recent studies have shown that AI and ML can be used to fight COVID-19. The objective of this article is to summarize the recent AI- and ML-based studies that have addressed the pandemic. From an initial set of 634 articles, a total of 49 articles were finally selected through an inclusion-exclusion process. In this article, we have explored the objectives of the existing studies (i.e., the role of AI/ML in fighting the COVID-19 pandemic); the context of the studies (i.e., whether it was focused on a specific country-context or with a global perspective; the type and volume of the dataset; and the methodology, algorithms, and techniques adopted in the prediction or diagnosis processes). We have mapped the algorithms and techniques with the data type by highlighting their prediction/classification accuracy. From our analysis, we categorized the objectives of the studies into four groups: disease detection, epidemic forecasting, sustainable development, and disease diagnosis. We observed that most of these studies used deep learning algorithms on image-data, more specifically on chest X-rays and CT scans. We have identified six future research opportunities that we have summarized in this paper. Impact Statement: Artificial intelligence (AI) and machine learning(ML) methods have been widely used to assist in the fight against COVID-19 pandemic. A very few in-depth literature reviews have been conducted to synthesize the knowledge and identify future research agenda including a previously published review on data science for COVID-19 in this article. In this article, we synthesized reviewed recent literature that focuses on the usages and applications of AI and ML to fight against COVID-19. We have identified seven future research directions that would guide researchers to conduct future research. The most significant of these are: develop new treatment options, explore the contextual effect and variation in research outcomes, support the health care workforce, and explore the effect and variation in research outcomes based on different types of data.

45 citations


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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20234
202240
2021243
2020241
2019228
2018119