Ijaz Ali

Image fusion, especially in the context of multi-focus image fusion, plays a crucial role in digital image processing by enhancing the clarity and detail of visual content through the combination of multiple source images. Traditional spatial domain methods often suffer from issues like spectral distortion and low contrast, which has led researchers to explore techniques in the frequency domain, such as the Discrete Cosine Transform (DCT). DCT-based methods are particularly valued for their computational efficiency, making them a strong alternative, especially in applications like image compression and fusion. This study focuses on DCT-based approaches, including variants that incorporate Singular Value Decomposition (SVD) and a combination of Correlation Coefficient with Energy-Correlation (Corr_Eng), both with and without Consistency Verification (CV). Extensive testing on multi-focus image datasets revealed that the DCT + SVD + CV method consistently shows better results in both qualitative and quantitative assessments. This indicates that integrating DCT+SVD+CV provides a powerful approach for achieving effective and efficient image fusion.

In today’s globalized economic landscape, the assurance of economic stability is of paramount importance, necessitating precise financial decision-making and policy formulation. This assurance is significantly augmented by innovative approaches to predicting crude oil prices, particularly in the context of energy stock markets denominated in USD. This paper delves into the transformative effect of accurate crude oil price prediction on economic policy stability. It underscores the challenges and limitations posed by policy uncertainties and emphasizes the pivotal role of innovative solutions in mitigating these challenges. Moreover, it recognizes the imperative need for secure data storage to facilitate the application of machine learning in this domain. Furthermore, effective management and regulation of power grid systems are explored as indispensable strategies for tempering the volatility introduced by fluctuations in energy stock markets. As we work to address these gaps in knowledge, the potential for sustainable power systems to supersede fossil fuels emerges as a driving force behind the maintenance of stable economic policies.

As criminal activity increasingly relies on digital devices, the field of digital forensics plays a vital role in identifying and investigating criminals. In this paper, we addressed the problem of anomaly detection in digital forensics data. Our objective was to propose an effective approach for identifying suspicious patterns and activities that could indicate criminal behavior. To achieve this, we introduce a novel method called the Novel Support Vector Neural Network (NSVNN). We evaluated the performance of the NSVNN by conducting experiments on a real-world dataset of digital forensics data. The dataset consisted of various features related to network activity, system logs, and file metadata. Through our experiments, we compared the NSVNN with several existing anomaly detection algorithms, including Support Vector Machines (SVM) and neural networks. We measured and analyzed the performance of each algorithm in terms of the accuracy, precision, recall, and F1-score. Furthermore, we provide insights into the specific features that contribute significantly to the detection of anomalies. Our results demonstrated that the NSVNN method outperformed the existing algorithms in terms of anomaly detection accuracy. We also highlight the interpretability of the NSVNN model by analyzing the feature importance and providing insights into the decision-making process. Overall, our research contributes to the field of digital forensics by proposing a novel approach, the NSVNN, for anomaly detection. We emphasize the importance of both performance evaluation and model interpretability in this context, providing practical insights for identifying criminal behavior in digital forensics investigations.

As the network is closely related to people’s daily life, network security has become an important factor affecting the physical and mental health of human beings. Network flow classification is the foundation of network security. It is the basis for providing various network services such as network security maintenance, network monitoring, and network quality of service (QoS). Therefore, this field has always been a hot spot of academic and industrial research. Existing studies have shown that through appropriate data preprocessing techniques, machine learning methods can be used to classify network flows, most of which, however, are based on manually and expert‐originated feature sets; it is a time‐consuming and laborious work. Moreover, only features extracted by a single model can be used in classification tasks, which can easily make the model inefficient and prone to overfitting. In order to solve the abovementioned problems, this study proposes a multimodal automatic analysis framework based on spatial and sequential features. The framework is completely based on the deep learning method and realizes automatic extraction of two types of features, which is very suitable for processing large‐flow information; this improves the efficiency of network flow classification. There are two types of frameworks based on pretraining and joint‐training, respectively, with analyzing the advantages and disadvantages of them in practice. In terms of evaluation, compared with the previous methods, the experimental results show that the framework has good performance in both accuracy and stability.