Android has a large number of users that are accumulating with each passing day. Security of the Android ecosystem is a major concern for these users with the provision of quality services. In this ...paper, multimodal analysis of malware apps has been presented. We exploit static, dynamic, and visual features of apps to predict the malicious apps using information fusion. The proposed study applies case-based reasoning; for catalyzing the process of training and validation over renowned datasets with enriched feature-set. Our proposed semi-supervised technique uses benign and malicious apps to predict and classify malware. The prediction process uses a hybrid analysis of malware. The proposed approach, due to the efficient and adaptive nature of CBR, outperforms prevalent approaches. Our approach has an accuracy of 95% and reduced rate of false negative rate and a better precision metric, which beat the state-of-the-art techniques.
Digitalization in healthcare through advanced methods, tools, and the Internet are prominent social development factors. However, hackers and malpractices through cybercrimes made this digitalization ...worrisome for policymakers. In this study, the role of E-Government Development as a proxy for digitalization and corruption prevalence has been analyzed in Healthcare sustainability in developing and underdeveloped countries of Asia from 2015 to 2021. Moreover, a moderator role of Cybersecurity measures has also been estimated on EGDI, CRP, and HS through the two-step system GMM estimation. The results show that EGDI and CRP control measures significantly improved HS in Asia. Furthermore, by deploying strong and effective Cybersecurity measures, Asia’s digitalization and institutional practices are considerably enhanced, which also has an incremental impact on HS and ethical values. This present study added a novel contribution to existing digitalization and public health services literature and empirical analysis by comprehensively applying advanced econometric estimation. The study concludes that cybersecurity measures significantly improved healthcare digitalization and controlled the institutional malfunctioning in Asia. This study gives insight into how cybersecurity measures enhance the service quality and promote institutional quality of the health sector in Asia, which will help draft sustainable policy decisions and ethical values in the coming years.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
PurposeE-government development (EGD) is vital in enhancing the institutional quality and sustainable public service (SPS) delivery by eradicating corruption and cybersecurity ...crimes.Design/methodology/approachThe present study applied econometric fixed-effect (FE) regression analysis and random forest (RF) algorithm through machine learning for comprehensive estimations in achieving SPS. This study gauges the nexus between the EGD as an independent variable and public service sustainability (PSS) as a proxy of public health services as a dependent variable in the presence of two moderators, corruption and cybersecurity indices from 47 Asian countries economies from 2015 to 2019.FindingsThe computational estimation and econometric findings show that EGD quality has improved with time in Asia and substantially promoted PSS. It further explores that exercising corruption control measures and introducing sound cybersecurity initiatives enhance PSS's quality and support the EDG effect much better.Practical implicationsThe study concludes that E-Government has positively impacted PSS (healthcare) in Asia while controlling cybersecurity and institutional malfunctioning made an E-Government system healthier and SPS development in Asia.Originality/valueThis study added a novel contribution to existing E-Government and public services literature by comprehensively applied FE regression and RF algorithm analysis. Moreover, E-Government and cybersecurity improvement also has taken under consideration for PSS in Asian economies.
Intelligent robotics and expert system applications in dentistry suffer from identification and detection problems due to the non-uniform brightness and low contrast in the captured images. Moreover, ...during the diagnostic process, exposure of sensitive facial parts to ionizing radiations (e.g., X-Rays) has several disadvantages and provides a limited angle for the view of vision. Capturing high-quality medical images with advanced digital devices is challenging, and processing these images distorts the contrast and visual quality. It curtails the performance of potential intelligent and expert systems and disincentives the early diagnosis of oral and dental diseases. The traditional enhancement methods are designed for specific conditions, and network-based methods rely on large-scale datasets with limited adaptability towards varying conditions. This paper proposed a novel and adaptive dental image enhancement strategy based on a small dataset and proposed a paired branch Denticle-Edification network (Ded-Net). The input dental images are decomposed into reflection and illumination in a multilayer Denticle network (De-Net). The subsequent enhancement operations are performed to remove the hidden degradation of reflection and illumination. The adaptive illumination consistency is maintained through the Edification network (Ed-Net). The network is regularized following the decomposition congruity of the input data and provides user-specific freedom of adaptability towards desired contrast levels. The experimental results demonstrate that the proposed method improves visibility and contrast and preserves the edges and boundaries of the low-contrast input images. It proves that the proposed method is suitable for intelligent and expert system applications for future dental imaging.
Abstract
Online social networks (OSNs) have reduced global boundaries, with Twitter enabling perspective sharing. Bot profile‐propagated false information misuse raises serious concerns. Considering ...this issue, we present our research on classifying Twitter accounts as “human” or “bot” using deep neural networks and transfer learning. Our proposed approach, TL‐PBot, stands for bot profile detection using transfer learning. The TL‐PBot framework utilizes Twitter account metadata such as follower count. Our TL‐PBot also incorporates text data from the Twitter description field as a feature. Word representation of the text data is achieved using Global Vectors (GloVe), a pre‐trained model. By employing user profile‐based features, we significantly reduce the overhead of feature engineering. The hybrid nature of the model enables it to effectively handle mixed‐type features, including text, binary, and numerical data. We design the network using long‐short‐term memory (LSTM) units. DNN model layers were trained, and the weights of the pre‐trained model layers were frozen to apply the transfer learning, resulting in reduced training time and improved bot profile detection accuracy. The performance of the proposed TL‐PBot is evaluated using publicly available datasets. The proposed approach is trained and tested on the same datasets and further evaluated on the validation datasets that were not used in the training phase, which is also a novelty in our approach. Comparative analysis with state‐of‐the‐art approaches demonstrates that the TL‐PBot approach achieves a higher accuracy of 98.07%, while excelling in precision of 99%, recall of 98%, f measure of 98.32%, and AUC of 0.99. Employing the transfer learning strategy resulted in an accelerated detection rate of 5.04 milliseconds, attesting to the effectiveness of this approach in enhancing computational efficiency.
Patients who have Alzheimer’s disease (AD) pass through several irreversible stages, which ultimately result in the patient’s death. It is crucial to understand and detect AD at an early stage to ...slow down its progression due to the non-curable nature of the disease. Diagnostic techniques are primarily based on magnetic resonance imaging (MRI) and expensive high-dimensional 3D imaging data. Classic methods can hardly discriminate among the almost similar pixels of the brain patterns of various age groups. The recent deep learning-based methods can contribute to the detection of the various stages of AD but require large-scale datasets and face several challenges while using the 3D volumes directly. The extant deep learning-based work is mainly focused on binary classification, but it is challenging to detect multiple stages with these methods. In this work, we propose a deep learning-based multiclass classification method to distinguish amongst various stages for the early diagnosis of Alzheimer’s. The proposed method significantly handles data shortage challenges by augmentation and manages to classify the 2D images obtained after the efficient pre-processing of the publicly available Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Our method achieves an accuracy of 98.9% with an F1 score of 96.3. Extensive experiments are performed, and overall results demonstrate that the proposed method outperforms the state-of-the-art methods in terms of overall performance.
Sensitive Data requires encryption before uploading to a public cloud. Access control based on Attribute-Based Encryption (ABE) is an effective technique to ensure data shared security and privacy in ...the public cloud. Cipher-Text Policy of ABE may suffer from scalability and performance issues as they do not permit for addition or removal of computing nodes at run time. Furthermore, another problem is existing approaches suffer from single-point-of-failure (SPoF). Therefore, we introduce a scalable multi-agent system architecture based on CP-ABE to ensure data sharing on public cloud storage and reliability in our proposed work. We proposed a cloud host as an inter-mediator between the user and the authorized agents without violating the system's privacy and security. We have also proposed a novel methodology to protect the cloud from malware by exploiting the state's efficient power of the art Gemini approach. Gemini is an efficient methodology for binary code-based graph embedding similarity detection. Our proposed study overcomes the deficiencies of scalability and efficiency along with providing the mechanism for malware detection in the cloud. It covers three aspects: scalability, the efficiency with multi-agents, and malware detection capability. Our contributed work is scalable, efficient for cloud data sharing, and protects from malware. Results reveal that our work provides better performance with preserving the security, privacy, and fine granularity features of CP-ABE and malware screening using regress analysis by the graph embedding technique.
Floating treatment wetlands (FTWs) are considered as cost-effective remediation options for various types of wastewater. Their effectiveness has been shown in several lab-scale and pilot-scale ...studies; however, there is a paucity of published data on pilot-scale systems treating genuine wastewater. This study aims to assess the performance of a pilot-scale system, carrying Phragmites australis in combination with three plant growth promoting and dye-degrading bacteria (Acinetobacter junii strain NT-15, Rhodococcus sp. strain NT-39, and Pseudomonas indoloxydans strain NT-38) for the treatment of textile industry wastewater (Interloop Limited, Faisalabad, Pakistan). Fifteen FTW macrocosms were established comprising plants and bacteria separately or in combination. Each unit was capable to carry 1000 L of the wastewater and the system was operated in a batch-wise mode for the period of 2 years. After a year of installation, performance of all FTWs units was optimal. A high removal in organic and inorganic pollutants was observed in the vegetated tanks, whereas combined application of plants and bacteria further enhanced the removal performance, i.e., chemical oxygen demand was reduced to 92%, biochemical oxygen demand to 91%, color to 86%, and trace metals to approximately 87% in the wastewater. The augmented bacteria displayed persistence in water as well as in the roots and shoots of P. australis suggesting a potential partnership with the host towards enhanced performance. Treated wastewater met the National Environmental Quality Standards (NEQS) set by the Government of Pakistan, thus, suggesting its discharge in the surface water without any potential risks. This pilot-scale study is a step forward towards sustainable remediation of the textile wastewater in the field.
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•Bacterially assisted FTWs macrocosms were engineered to treat real textile effluent.•Combined application of plants and bacteria had a prominent effect on pollution reduction.•Inoculated bacteria displayed persistence in different components of the FTWs.•Treated wastewater met the National Environmental Quality Standards (NEQS) of Pakistan.
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•Low and ill-light image enhancement.•Lowlight mage enhancement without paired training data supervision.•Image enhancement with a few-shots of training data.•Deep hybrid learning, ...independent of the type of training and test data.•First Large scale dataset for ill-lighting conditions.
Intelligent system applications in computer vision suffer detection and identification problems in ill lighting conditions (i.e., non-uniform illumination), where under-exposed and over-exposed regions coexist in the captured images. Processing on these images results in over and under enhancement with colour and contrast distortions. The traditional methods design some handcrafted constraints and rely on image pairs and priors, whereas existing deep learning-based methods rely on large scale and even paired training data. But these method’s capacity is limited to specific scenes (i.e., lighting conditions). In this paper, we present a deep-hybrid ill-light image enhancement method and propose a contrast enhancement strategy based on the decomposition of the input images into reflection J and illumination T. A Divide to Glitter network (D2G-Net) is designed to learn from the few-shots of training samples and do not require paired and large quantity training data. D2G-Net is comprised of a multilayer Division-Net for image division and a Glitter-Net to amplify the illumination map. We propose to regularize learning using a correlation consistency of decomposition extracted from the input data itself. Extensive experiments are organized under ill-lighting conditions, where a new test dataset is also proposed with robust lighting variation to evaluate the performance of the proposed method. Experimental results prove that our method has superior performance for preserving structural and texture details compared to state-of-the-art approaches, which suggests that our method is more practical in interactive computer vision and intelligent expert system applications.
Productivity of an orchard generally depends upon the fertility of the soil and the nutrient requirements of the fruit trees. Phosphorus (P) extractability from soils influences the P sorption, ...release patterns, and P bioavailability. A study was carried out to investigate P extractability
via
seven extraction methods in relation to soil properties in three fruit orchards. In total, 10 soil samples were collected from each fruit orchard, namely, citrus (
Citrus sinensis
L.), loquat (
Eriobotrya japonica
L.), and guava (
Psidium guajava
L.), located in similar ecological conditions to the Haripur district of Pakistan. Available P in the soil was extracted using deionized H
2
O, CaCl
2
, Mehlich 1, Bray 1, Olsen, HCl, and DTPA methods. Selected soil properties pH, electrical conductivity (EC), soil organic matter (SOM), texture, cation exchange capacity (CEC), macronutrients, and micronutrients were also determined. Soils sampled from orchards indicated significant differences in soil properties. Orchards have sequestered more amount of C stock in soil than without an orchard. The extractability of P from soils was profoundly affected by P extraction methods. The average amount of extractable P was relatively higher in those soils where the total amount of P was also higher. These methods extracted different pools of soil P with varying P concentrations regulated by the soil properties. Phosphorus amounts extracted were varied in the order of HCl > DTPA > Mehlich 1 > Bray 1 > Olsen > CaCl
2
> water. Among orchards, a higher amount of P was found in soils of loquat followed by citrus and guava orchards. Regardless of the method, subsurface soil got a lower concentration of extractable P than surface soil in all orchards. The extractable P was highly associated with soil properties. DTPA extractable P was related to SOM soil clay content and CEC by
R
2
values of 0.83, 0.87, and 0.78, respectively. Most of the extraction methods were positively correlated with each other. This study indicated that SOM inputs and turnover associated with orchard trees exhibited a substantial quantity of extractable P in soils. Predicting available P in relation to its bioavailability using these methods in contrasting soils is required.
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