Khurasan University

The increasing frequency and sophistication of cyber-attacks have exposed significant shortcomings in conventional detection systems, emphasizing the urgent need for more advanced Cyber Threat Intelligence (CTI) capabilities. While Open-Source Intelligence (OSINT) has become a cornerstone for early threat identification, the manual analysis of such unstructured data remains labor-intensive and susceptible to error, limiting its effectiveness. To address these challenges, this study introduces an AI-driven system designed for real-time detection and analysis of cyber threat information on Twitter. The approach integrates a hybrid feature extraction technique that combines Bidirectional Encoder Representations from Transformers (BERT) with Iterated Dilated Convolutional Neural Networks (ID-CNNs). To enhance feature relevance and remove noise and redundant features, the Binary Tree Growth (BTG) algorithm is employed as feature selection. Classification is performed using a bi-directional temporal convolutional network (BiTCN), which is well-suited for modeling sequential data. Experimental evaluations show that the proposed model achieves strong performance, with accuracy rates of 99.01% on dataset 1 and 97.63% on dataset 2 using 10-fold cross-validation. The proposed ID-BERT-BiTCN outperforms other machine learning (ML) and existing models. These results highlight the model’s potential to enhance the effectiveness of CTI by enabling timely and accurate threat detection from social media sources.

This paper aimed to identify the sources, types, challenges, and impacts of plastic pollution in Freetown, providing a comprehensive situational analysis. The study employed a qualitative and integrative literature review, contextualizing data from various sources, including government reports and international organizations. Databases such as Google Scholar, Scopus, and Web of Science were utilized to compile and analyze relevant information. Findings showed that 80% of Freetown's 40,600 tons/year of plastic waste is dumped in the streets. Sachet bags are the dominant, accounting for 70-80% of plastic waste, mainly from households. The study reveals that plastic pollution in Freetown is a critical environmental issue exacerbated by inadequate waste management infrastructure, rapid urbanization, and socio-economic challenges. Key impacts include threats to public health, disruption of marine and terrestrial ecosystems, and significant economic costs, particularly in the tourism and fishing industries. The study underscores the urgent need for policy interventions, such as banning single-use plastics, enhancing waste management systems, and promoting community engagement in recycling efforts. The broader implications of this research suggest that addressing plastic pollution in Freetown could significantly contribute to global sustainable development goals (SDGs), particularly those related to poverty alleviation, health, and environmental sustainability. Finally, the findings offer valuable insights for policymakers, urging them to implement comprehensive waste management strategies and to engage local communities in efforts to mitigate plastic pollution, thereby fostering a more sustainable urban environment.

Several studies have suggested that leaders showing high levels of authentic leadership increase workers’ engagement and job satisfaction. The study was conducted to examine the effect of authentic leadership on employees’ engagement and job satisfaction. The study focused in the public sector universities operating in Eastern Zone. The faculty members working at Nangarhar University, Laghman University, Sayed Jamaluddin Afghan University and Nuristan Institute of Higher Education was taken as the target population of the study. From these employees, 143 were selected as a sample of the study using proportional allocation method. Descriptive, regression techniques and reliability statistics were used to analyze the data. As per the results of reliability statistics, scales for authentic leadership (.827), employees’ engagement (.924) and job satisfaction (.744) were found highly reliable. According to regression results of testing hypotheses, the result of authentic leadership has positive significant effects on employee engagement and on job satisfaction. This research will help academic professionals in public sector universities to know how authentic leadership style influences positively employees’ engagement and job satisfaction.

Breast cancer is one of the leading causes of increasing deaths in women worldwide. The complex nature (microcalcification and masses) of breast cancer cells makes it quite difficult for radiologists to diagnose it properly. Subsequently, various computer-aided diagnosis (CAD) systems have previously been developed and are being used to aid radiologists in the diagnosis of cancer cells. However, due to intrinsic risks associated with the delayed and/or incorrect diagnosis, it is indispensable to improve the developed diagnostic systems. In this regard, machine learning has recently been playing a potential role in the early and precise detection of breast cancer. This paper presents a new machine learning-based framework that utilizes the Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and Multilayer Perception approaches to efficiently predict breast cancer from the patient data. For this purpose, the Wisconsin Diagnostic Breast Cancer (WDBC) dataset has been utilized and classified using a hybrid Multilayer Perceptron Model (MLP) and 5-fold cross-validation framework as a working prototype. For the improved classification, a connection-based feature selection technique has been used that also eliminates the recursive features. The proposed framework has been validated on two separate datasets, i.e., the Wisconsin Prognostic dataset (WPBC) and Wisconsin Original Breast Cancer (WOBC) datasets. The results demonstrate improved accuracy of 99.12% due to efficient data preprocessing and feature selection applied to the input data.

In recent years, the development of smart transportation has accelerated research on semantic segmentation as it is one of the most important problems in this area. A large receptive field has always been the center of focus when designing convolutional neural networks for semantic segmentation. A majority of recent techniques have used maxpooling to increase the receptive field of a network at an expense of decreasing its spatial resolution. Although this idea has shown improved results in object detection applications, however, when it comes to semantic segmentation, a high spatial resolution also needs to be considered. To address this issue, a new deep learning model, the M‐Net is proposed in this paper which satisfies both high spatial resolution and a large enough receptive field while keeping the size of the model to a minimum. The proposed network is based on an encoder‐decoder architecture. The encoder uses atrous convolution to encode the features at full resolution, and instead of using heavy transposed convolution, the decoder consists of a multipath feature extraction module that can extract multiscale context information from the encoded features. The experimental results reported in the paper demonstrate the viability of the proposed scheme.

RETRACTED: Teeth have a very meaningful function for humans which is to help chew, cut food. Therefore the teeth must be treated properly. Although treatment has been done but there are still changes in tooth color. This device can reduce human error from medical officers who conduct examinations. B

This research paper assess the influence of financial literacy on profitability of organizations. The data collected through questionnaire, which is adopted from different authors. The multiple regression analysis technique was utilized to obtain the results. The random sampling technique used to acquired the data. The main results of the study shows that financial literacy has a positive and statistically significant effect on profitability of firms. An unit rise would lead firms to rise their profitability. Similarly, the study indicates that debt management literacy has a positive and statistically significant effect on profitability of firms. An unit rise would lead firms to rise their profitability.