Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era. Malicious Uniform Resource Locators (URLs) can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their systems. This may result in compromised security of the systems, scams, and other such cyberattacks. These attacks hijack huge quantities of the available data, incurring heavy financial loss. At the same time, Machine Learning (ML) and Deep Learning (DL) models paved the way for designing models that can detect malicious URLs accurately and classify them. With this motivation, the current article develops an Artificial Fish Swarm Algorithm (AFSA) with Deep Learning Enabled Malicious URL Detection and Classification (AFSADL-MURLC) model. The presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine URLs. To attain this, AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding technique. In addition, the created vector model is then passed onto Gated Recurrent Unit (GRU) classification to recognize the malicious URLs. Finally, AFSA is applied to the proposed model to enhance the efficiency of GRU model. The proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle repository. The simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.

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The construction of an automatic voice pathology detection system employing machine learning algorithms to study voice abnormalities is crucial for the early detection of voice pathologies and identifying the specific type of pathology from which patients suffer. This paper’s primary objective is to construct a deep learning model for accurate speech pathology identification. Manual audio feature extraction was employed as a foundation for the categorization process. Incorporating an additional piece of information, i.e., voice gender, via a two-level classifier model was the most critical aspect of this work. The first level determines whether the audio input is a male or female voice, and the second level determines whether the agent is pathological or healthy. Similar to the bulk of earlier efforts, the current study analyzed the audio signal by focusing solely on a single vowel, such as /a/, and ignoring phrases and other vowels. The analysis was performed on the Saarbruecken Voice Database,. The two-level cascaded model attained an accuracy and F1 score of 88.84% and 87.39%, respectively, which was superior to earlier attempts on the same dataset and provides a steppingstone towards a more precise early diagnosis of voice complications.

Antibiotics become an emerging contaminant and receive more interests due to its ecotoxicological and strong stability in water ecosystems. Antibiotic adsorption onto carbon materials are biochars among the wastewater mechanisms. This research used machine learning (ML) techniques to generate general adsorption forecasting model for sulfamethoxazole (SMX) and tetracycline (TC) on CBM. Dirichlet design parameters and a combined combination of Neumann and Dirichlet boundary situation are applied to the system of differential equations. In addition, the proposed method use the learning under supervision technique of a nonlinear autoregressive for estimating the CO2 concentration and flows in units of rate of a reaction characteristics, an exogenous (NARX) neural network model with two activation functions was used (Log-sigmoid and hyperbolic tangent) and for both the findings of a TC and SMX absorption simulations showed the random forest performed support vector tree and nonlinear autoregressive exogenous neural networks and machine learning methods. Their relevance and complete dependency graph evaluation lead reasonable CBM uses for antimicrobial wastewater treatment. Also, machine learning forecasting model with good generalization capability is useful for building effective CBMs with few empirical screens. It evaluates the accuracy, precision, recall, false positive rate (FPR), and false negative rate (FNR) and also reduces the experimental screening.

Devices which are part of the Internet of Things (IoT) have strong connections; they generate and consume data, which necessitates data transfer among various devices. Smart gadgets collect sensitive information, perform critical tasks, make decisions based on indicator information, and connect and interact with one another quickly. Securing this sensitive data is one of the most vital challenges. A Network Intrusion Detection System (IDS) is often used to identify and eliminate malicious packets before they can enter a network. This operation must be done at the fog node because the Internet of Things devices are naturally low-power and do not require significant computational resources. In this same context, we offer a novel intrusion detection model capable of deployment at the fog nodes to detect the undesired traffic towards the IoT devices by leveraging features from the UNSW-NB15 dataset. Before continuing with the training of the models, correlation-based feature extraction is done to weed out the extra information contained within the data. This helps in the development of a model that has a low overall computational load. The Tab transformer model is proposed to perform well on the existing dataset and outperforms the traditional Machine Learning ML models developed as well as the previous efforts made on the same dataset. The Tab transformer model was designed only to be capable of handling continuous data. As a result, the proposed model obtained a performance of 98.35% when it came to classifying normal traffic data from abnormal traffic data. However, the model’s performance for predicting attacks involving multiple classes achieved an accuracy of 97.22%. The problem with imbalanced data appears to cause issues with the performance of the underrepresented classes. However, the evaluation results that were given indicated that the proposed model opened new avenues of research on detecting anomalies in fog nodes.