• Journal of Information Processing Systems
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• 제18권3호
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• pp.319-331
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• 2022

The capability to examine an X-ray image is so far a challenging task. In this work, we suggest a practical and novel algorithm based on image fusion to inspect the issues such as background noise, blurriness, or sharpness, which curbs the quality of dual-energy X-ray images. The current technology exercised for the examination of bags and baggage is "X-ray"; however, the results of the incumbent technology used show blurred and low contrast level images. This paper aims to improve the quality of X-ray images for a clearer vision of illegitimate or volatile substances. A dataset of 40 images was taken for the experiment, but for clarity, the results of only 13 images have been shown. The results were evaluated using MSE and PSNR metrics, where the average PSNR value of the proposed system compared to single X-ray images was increased by 19.3%, and the MSE value decreased by 17.3%. The results show that the proposed framework will help discern threats and the entire scanning process.

• The Journal of Asian Finance, Economics and Business
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• 제9권6호
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• pp.33-44
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• 2022

Women entrepreneurs play a vital role in employment creation, economic development, and growth. Women entrepreneurship is deep-rooted in the social and cultural norms and values of society. Women's entrepreneurship contribution is still invisible and needs to be properly investigated. The current research study explores "how institutions affect women's entrepreneurial performance in Pakistan" by using institutional and social cognitive theories. Focusing on the Formal and informal institutions, this research examines how institutions are affecting women's entrepreneurial performance by taking the mediating role of women's entrepreneurial self-efficacy and ethical decision making. A 7-point Likert scale research questionnaire is used to collect primary data. Data on active entrepreneurs are collected from the Peshawar, Mardan, and Abbottabad divisions of KPK's Women Chambers of Commerce. The data is empirically tested through the path analysis technique of structural equation modeling (SEM) through SMART PLS 3. The results indicated that women's entrepreneurial self-efficacy and ethical decision-making strongly mediate both institutions and significantly affect women's entrepreneurial performance. The study suggests that government and concerned departments should pay due attention to determinants like informal institutions and social constraints to boost women's entrepreneurial performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권10호
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• pp.3230-3255
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• 2022

Causality mining in NLP is a significant area of interest, which benefits in many daily life applications, including decision making, business risk management, question answering, future event prediction, scenario generation, and information retrieval. Mining those causalities was a challenging and open problem for the prior non-statistical and statistical techniques using web sources that required hand-crafted linguistics patterns for feature engineering, which were subject to domain knowledge and required much human effort. Those studies overlooked implicit, ambiguous, and heterogeneous causality and focused on explicit causality mining. In contrast to statistical and non-statistical approaches, we present Bidirectional Encoder Representations from Transformers (BERT) integrated with Multi-level Feature Networks (MFN) for causality recognition, called BERT+MFN for causality recognition in noisy and informal web datasets without human-designed features. In our model, MFN consists of a three-column knowledge-oriented network (TC-KN), bi-LSTM, and Relation Network (RN) that mine causality information at the segment level. BERT captures semantic features at the word level. We perform experiments on Alternative Lexicalization (AltLexes) datasets. The experimental outcomes show that our model outperforms baseline causality and text mining techniques.

• East Asian Economic Review
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• 제27권1호
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• pp.33-60
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• 2023

This paper analyzes the entire distribution of stock market returns/volatility in five emerging markets (ASEAN5) and figures out the conditional distribution of the CHI_EPU index. The aim is to examine the impact of CHI_EPU on the stock returns/volatility density of ASEAN5 markets. It also examined whether changes in CHI_EPU explain returns at higher or lower points (abnormal returns). This paper models the behaviour of stock returns from March 2011 to June 2018 using a non-parametric conditional density estimation approach. The results indicate that CHI_EPU diminishes stock returns and augments volatility in ASEAN5 markets, except for Malaysia, where it affects stock returns positively. The possible reason for this positive impact is that EPU is not the leading factor reducing Malaysian stock returns; but, other forces, such as dependency on other countries' stock markets and global factors, may have a positive impact on stock returns (Bachmann and Bayer, 2013). Thus, the risk of simultaneous investment in Chinese and ASEAN5 stock markets, except Malaysia, is high. Further, the degree of this influence intensifies at extreme high/low intervals (positive/negative tails). The findings of this study have significant implications for investors, policymakers, market agents, and analysts of ASEAN5.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권8호
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• pp.2381-2398
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• 2024

The construction of virtual indoor spaces is crucial for the development of metaverses, virtual production, and other 3D content domains. Traditional methods for creating these spaces are often cost-prohibitive and labor-intensive. To address these challenges, we present a pipeline for generating digital twins of real indoor environments from RGB-D camera-scanned data. Our pipeline synergizes space structure estimation, 3D object detection, and the inpainting of missing areas, utilizing deep learning technologies to automate the creation process. Specifically, we apply deep learning models for object recognition and area inpainting, significantly enhancing the accuracy and efficiency of virtual space construction. Our approach minimizes manual labor and reduces costs, paving the way for the creation of metaverse spaces that closely mimic real-world environments. Experimental results demonstrate the effectiveness of our deep learning applications in overcoming traditional obstacles in digital twin creation, offering high-fidelity digital replicas of indoor spaces. This advancement opens for immersive and realistic virtual content creation, showcasing the potential of deep learning in the field of virtual space construction.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• 제12권2호
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• pp.61-68
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• 2012

Recommender Systems have gained much popularity among researchers and is applied in a number of applications. The exponential growth of users and products poses some key challenges for recommender systems. Recommender Systems mostly suffer from scalability and accuracy. The accuracy of Recommender system is somehow inversely proportional to its scalability. In this paper we proposed a Context Aware Hybrid Recommender System using matrix reduction for Hybrid model and clustering technique for predication of item features. In our approach we used user item-feature rating, User Demographic information and context information i.e. specific time and day to improve scalability and accuracy. Our Algorithm produce better results because we reduce the dimension of items features matrix by using different reduction techniques and use user demographic information, construct context aware hybrid user model, cluster the similar user offline, find the nearest neighbors, predict the item features and recommend the Top N- items.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권10호
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• pp.4844-4864
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• 2017

Interference-aware routing protocol design for underwater wireless sensor networks (UWSNs) is one of the key strategies in reducing packet loss in the highly hostile underwater environment. The reduced interference causes efficient utilization of the limited battery power of the sensor nodes that, in consequence, prolongs the entire network lifetime. In this paper, we propose an energy-efficient interference-aware routing (EEIAR) protocol for UWSNs. A sender node selects the best relay node in its neighborhood with the lowest depth and the least number of neighbors. Combination of the two routing metrics ensures that data packets are forwarded along the least interference paths to reach the final destination. The proposed work is unique in that it does not require the full dimensional localization information of sensor nodes and the network total depth is segmented to identify source, relay and neighbor nodes. Simulation results reveal better performance of the scheme than the counterparts DBR and EEDBR techniques in terms of energy efficiency, packet delivery ratio and end-to-end delay.

• Asian Pacific Journal of Cancer Prevention
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• 제17권5호
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• pp.2485-2490
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• 2016

The liver is one of the most common sites of cancer in the world, hepatocellular carcinoma (HCC) predominating. HCC is the sixth most common cancer and the third leading cause of cancer related death overall. Hepatitis C is a major risk factor and HCV is a rapid spreading virus which has become a problem globally, including in Pakistan. Interferon alpha therapy is used against HCV disease to regulate cell reproduction and to boost the immune system. In minute amounts interferon alpha is produced naturally by the immune system in HCV patients in response to hepatitis C virus and binds to receptors in the target cells and starts transcription of 20-30 genes due to which it develops an antiviral influence. Interferon is also administered artificially to overcome HCV disease and remove the biological effect of the virus from the infected site. The use of interferon or Peg-IFN plus Ribavirin treatment is also associated with adverse effects on body. For the current study, a convenient sample of 156 HCV positive patients of both males and females were taken. To collect blood CP and ALT, a reduction of level data and other important information were collected from the patients at regular intervals. Findings were 11.4 % in the red blood cells (RBC), 9.64 % in the total leukocyte count (WBC), 8.4 % in the hemoglobin levels (HB), 30.3 % in the platelet (Plt) count in both sexes. There was significant reduction in ALT levels due to Pegylated interferon plus ribavirin therapy. Hence strict haemotological monitoring of blood CP and ALT levels is necessary at regular intervals to reduce severe side effects which may lead to morbidity and mortality.

• Journal of Electrical Engineering and Technology
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• 제11권2호
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• pp.529-536
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• 2016

In this paper we proposed an unsupervised algorithm to estimate the reverberation time (RT) directly from the reverberant speech signal. For estimation process we use maximum likelihood estimation (MLE) which is a very well-known and state of the art method for estimation in the field of signal processing. All existing RT estimation methods are based on the decay rate distribution. The decay rate can be obtained either from the energy envelop decay curve analysis of noise source when it is switch off or from decay curve of impulse response of an enclosure. The analysis of a pre-existing method of reverberation time estimation is the foundation of the proposed method. In one of the state of the art method, the reverberation decay is modeled as a Laplacian distribution. In this paper, the proposed method models the reverberation decay as a Gamma distribution along with the unification of an effective technique for spotting free decay in reverberant speech. Maximum likelihood estimation technique is then used to estimate the RT from the free decays. The method was motivated by our observation that the RT of a reverberant signal when falls in specific range, then the decay rate of the signal follows Gamma distribution. Experiments are carried out on different reverberant speech signal to measure the accuracy of the suggested method. The experimental results reveal that the proposed method performs better and the accuracy is high in comparison to the state of the art method.

• Journal of Microbiology and Biotechnology
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• 제19권10호
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• pp.1244-1249
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• 2009

Endophytic fungi are known for the production of valuable metabolites, but information on the gibberellin production capacity of this group is limited. We isolated 9 endophytic fungi from the roots of salt-stressed soybean plants and screened them on waito-c rice, in order to identify plant growth promoting fungal strains. The fungal isolate TK-2-4 gave maximum plant length (20.35 cm) promotion in comparison with wild-type Gibberella fujikuroi (19.5 cm). In a separate experiment, bioassay of TK-2-4 promoted plant length and biomass of soybean cultivar Taegwangkong. The TK-2-4 culture filtrate was analyzed for the presence of gibberellins, and it was found that all physiologically active gibberellins, especially $GA_4$ and $GA_7$, were present in higher amounts ($GA_1$, 0.11 ng/ml; $GA_3$, 2.91 ng/ml; $GA_4$, 3.21 ng/ml; and $GA_7$, 1.4 ng/ml) in conjunction with physiologically inactive $GA_9$ (0.05 ng/ml), $GA_{12}$ (0.23 ng/ ml), $GA_{15}$ (0.42 ng/ml), $GA_{19}$ (0.53 ng/ml), and $GA_{20}$ (0.06 ng/ml). The fungal isolate TK-2-4 was later identified as a new strain of Phoma herbarum, through the phylogenetic analysis of 28S rDNA sequence.