Search Results (5)

Despite significant advancements in object detection technology, most existing detection networks fail to investigate global aspects while extracting features from the inputs and cannot automatically adjust based on the characteristics of the inputs. The present study addresses this problem by proposing a detection network consisting of three stages: preattention, attention, and prediction. In the preattention stage, the network framework is automatically selected based on the features of the images’ objects. In the attention stage, the transformer structure is introduced. Taking into account the global features of the target, this study combines a self-attention module in the transformer model and convolution operation to integrate image features from global to local and for detection, thus improving the ship target accuracy. This model uses mathematical methods to obtain results of predictive testing in the prediction stage. The above improvements are based on the You Only Look Once version 4 (YOLOv4) framework, named “Auto-T-YOLO”. The model achieves the highest accuracy of 96.3% on the SAR Ship Detection dataset (SSDD) compared to the other state-of-the-art (SOTA) model. It achieves 98.33% and 91.78% accuracy in the offshore and inshore scenes, respectively. The experimental results verify the practicality, validity, and robustness of the proposed model. Full article

Modern information technology (IT) is well developed, and almost everyone uses the features of IT and services within the Internet. However, people are being affected due to cybersecurity threats. People can adhere to the recommended cybersecurity guidelines, rules, adopted standards, and cybercrime preventive measures to largely mitigate these threats. The ignorance of or lack of cybersecurity knowledge also causes a critical problem regarding confidentiality and privacy. It is not possible to fully avoid cybercrimes that often lead to sufficient business losses and spread forbidden themes (disgust, extremism, child porn, etc.). Therefore, to reduce the risk of cybercrimes, a web-based Blockchain-enabled cybersecurity awareness program (WBCA) process is introduced in this paper. The proposed WBCA trains users to improve their security skills. The proposed program helps with understanding the common behaviors of cybercriminals and improves user knowledge of cybersecurity hygiene, best cybersecurity practices, modern cybersecurity vulnerabilities, and trends. Furthermore, the proposed WBCA uses Blockchain technology to protect the program from potential threats. The proposed program is validated and tested using real-world cybersecurity topics with real users and cybersecurity experts. We anticipate that the proposed program can be extended to other domains, such as national or corporate courses, to increase the cybersecurity awareness level of users. A CentOS-based virtual private server is deployed for testing the proposed WBCA to determine its effectiveness. Finally, WBCA is also compared with other state-of-the-art web-based programs designed for cybersecurity awareness. Full article

Currently, life cannot be imagined without the use of bank cards for purchases or money transfers; however, their use provides new opportunities for money launderers and terrorist organizations. This paper proposes a blockchain-enabled transaction scanning (BTS) method for the detection of anomalous actions. The BTS method specifies the rules for outlier detection and rapid movements of funds, which restrict anomalous actions in transactions. The specified rules determine the specific patterns of malicious activities in the transactions. Furthermore, the rules of the BTS method scan the transaction history and provide a list of entities that receive money suspiciously. Finally, the blockchain-enabled process is used to restrict money laundering. To validate the performance of the proposed BTS method, a Spring Boot application is built based on the Java programming language. Based on experimental results, the proposed BTS method automates the process of investigating transactions and restricts money laundering incidents. Full article

In order to detect and control the concentration of polymetallic ions in industrial wastewater in real time, a spectrophotometric method combining wavelet transform (WT) and partial least squares regression (PLSR) is proposed for the simultaneous determination of zinc, cobalt and nickel in industrial wastewater by ultraviolet-visible spectrometry, without a separation step. WT was found to be suitable for spectral preprocessing, which effectively eliminated the noise, enhanced spectral feature information, improved the linearity of the detected ions and increased the number of selectable modeling wavelengths. PLSR was used to study the simultaneous detection of zinc, cobalt and nickel. The linear detection ranges were 10–100 mg/L for zinc, 0.6–6.0 mg/L for nickel and 0.3–3.0 mg/L for cobalt. The average relative deviation for zinc, nickel and cobalt was 2.85%, 3.05% and 2.24%, respectively. The results indicated that the WT–PLSR method is suitable for the online detection of polymetallic ions by ultraviolet-visible spectroscopy in zinc industrial wastewater. Full article

While emerging economies face the challenge of competing with developed nations, they are capable of catching up to the developed world. In this context, financial development and the degree of economic openness may provide better living conditions for the current generation without giving up future generations’ prosperity. Therefore, this research’s prime intention is to investigate the impact of economic openness and financial development on economic progress, employing Pakistan’s time-series data from 1975–2018. To examine the long-term association between economic openness, financial development, and economic progress, Autoregressive Distributed Lag (ARDL) cointegration tests were performed and the results present a long-term association between these variables. Findings from ARDL estimates indicate that the relationship between financial development and economic progress is significantly positive in the long term. Contrastingly, the relationship between economic openness and economic progress is significantly positive in the short term. A fully modified ordinary least square technique was applied to check the robustness of the long-term links. The Granger causality test revealed that economic progress is motivated by both economic openness and financial development in an emerging economy such as Pakistan. Thus, policies boosting financial development and economic openness are proposed to put the emerging economies on a path of sustainable economic development. Full article