Recently, the identification of human text and ChatGPT-generated text has become a hot research topic. The current study presents a Tunicate Swarm Algorithm with Long Short-Term Memory Recurrent Neural Network (TSA-LSTMRNN) model to detect both human as well as ChatGPT-generated text. The purpose of the proposed TSA-LSTMRNN method is to investigate the model’s decision and detect the presence of any particular pattern. In addition to this, the TSA-LSTMRNN technique focuses on designing Term Frequency–Inverse Document Frequency (TF-IDF), word embedding, and count vectorizers for the feature extraction process. For the detection and classification processes, the LSTMRNN model is used. Finally, the TSA is employed for selecting the parameters for the LSTMRNN approach, which enables improved detection performance. The simulation performance of the proposed TSA-LSTMRNN technique was investigated on benchmark databases, and the outcome demonstrated the advantage of the TSA-LSTMRNN system over other recent methods with a maximum accuracy of 93.17% and 93.83% on human- and ChatGPT-generated datasets, respectively.

Recently, artificial intelligence (AI) has gained an abundance of attention in cybersecurity for Industry 4.0 and has shown immense benefits in a large number of applications. AI technologies have paved the way for multiscale security and privacy in cybersecurity, namely AI-based malicious intruder protection, AI-based intrusion detection, prediction, and classification, and so on. Moreover, AI-based techniques have a remarkable potential to address the challenges of cybersecurity that Industry 4.0 faces, which is otherwise called the IIoT. This manuscript concentrates on the design of the Golden Jackal Optimization with Deep Learning-based Cyberattack Detection and Classification (GJODL-CADC) method in the IIoT platform. The major objective of the GJODL-CADC system lies in the detection and classification of cyberattacks on the IoT platform. To obtain this, the GJODL-CADC algorithm presents a new GJO-based feature selection approach to improve classification accuracy. Next, the GJODL-CADC method makes use of a hybrid autoencoder-based deep belief network (AE-DBN) approach for cyberattack detection. The effectiveness of the AE-DBN approach can be improved through the design of the pelican optimization algorithm (POA), which in turn improves the detection rate. An extensive set of simulations were accomplished to demonstrate the superior outcomes of the GJODL-CADC technique. An extensive analysis highlighted the promising performance of the GJODL-CADC technique compared to existing techniques.

Financial technology (Fintech) plays a pivotal role in driving contemporary technology, society, economies, and many other fields. The new-generation Fintech is Smart Fintech, mainly empowered and inspired by data science and artificial intelligence (DSAI) technologies. Smart Fintech combines DSAI and transforms finance and economies for driving automated, intelligent, personalized financial and economic businesses, services and systems, and the whole of business. The strength and growth of the country’s economy were evaluated with the accurate prediction of how many companies will succeed and how many will fail. Financial crisis prediction (FCP) has a considerable effect on the economy. Prior research focuses mainly on deep learning (DL), machine learning (ML), and statistical approaches for forecasting the financial health of a company. Thus, this study presents a hybrid hunter–prey optimization with a deep learning-based FCP (HHPODL-FCP) technique. The objective of the HHPODL-FCP algorithm lies in the effective identification of the financial crisis in enterprises or organizations. To accomplish this, the HHPODL-FCP method makes use of the HHPO algorithm for the feature subset selection process. In addition, the HHPODL-FCP technique employs the gated attention recurrent network (GARN) model for the identification and classification of financial and non-financial crises. The HHPODL-FCP method exploits a sparrow search algorithm (SSA)-based hyperparameter tuning process to enrich the performance of the GARN model. The simulation results of the HHPODL-FCP method are tested on different financial datasets. A wide range of experiments highlighted the remarkable performance of the HHPODL-FCP method over recent techniques under various measures.

The Emergency Departments (EDs), in hospitals located in a few important areas in Saudi Arabia, experience a heavy inflow of patients due to viral illnesses, pandemics, and even on a few special occasions events such as Hajj or Umrah, when pilgrims travel from one region to another with severe disease conditions. Apart from the EDs, it is critical to monitor the movements of patients from EDs to other wards inside the hospital or in the region. This is to track the spread of viral illnesses that require more attention. In this scenario, Machine Learning (ML) algorithms can be used to classify the data into many classes and track the target audience. The current research article presents a Machine Learning-based Medical Data Monitoring and Classification Model for the EDs of the KSA hospitals and is named MLMDMC-ED technique. The most important aim of the proposed MLMDMC-ED technique is to monitor and track the patient’s visits to the EDs, the treatment given to them based on the Canadian Emergency Department Triage and Acuity Scale (CTAS), and their Length Of Stay (LOS) in the hospital, based on their treatment requirements. A patient’s clinical history is crucial in terms of making decisions during health emergencies or pandemics. So, the data should be processed so that it can be classified and visualized in different formats using the ML technique. The current research work aims at extracting the textual features from the patients’ data using the metaheuristic Non-Defeatable Genetic Algorithm II (NSGA II). The data, collected from the hospitals, are classified using the Graph Convolutional Network (GCN) model. Grey Wolf Optimizer (GWO) is exploited for fine-tuning the parameters to optimize the performance of the GCN model. The proposed MLMDMC-ED technique was experimentally validated on the healthcare data and the outcomes indicated the improvements of the MLMDMC-ED technique over other models with a maximum accuracy of 91.87%.

Solid biofuels and Internet of Things (IoT) technologies play a vital role in the development of smart cities. Solid biofuels are a renewable and sustainable source of energy obtained from organic materials, such as wood, agricultural residues, and waste. The integration of IoT technology with solid biofuel classification can improve the performance, quality control, and overall management of biofuel production and usage. Recently, machine learning (ML) and deep learning (DL) models can be applied for the solid biofuel classification process. Therefore, this article develops a novel solid biofuel classification using sailfish optimizer hybrid deep learning (SBFC-SFOHDL) model in the IoT platform. The proposed SBFC-SFOHDL methodology focuses on the identification and classification of solid biofuels from agricultural residues in the IoT platform. To achieve this, the SBFC-SFOHDL method performs IoT-based data collection and data preprocessing to transom the input data into a compatible format. Moreover, the SBFC-SFOHDL technique employs the multihead self attention-based convolutional bidirectional long short-term memory model (MSA-CBLSTM) for solid biofuel classification. For improving the classification performance of the MSA-CBLSTM model, the SFO algorithm is utilized as a hyperparameter optimizer. The simulation results of the SBFC-SFOHDL technique are tested and the results are examined under different measures. An extensive comparison study reported the betterment of the SBFC-SFOHDL technique compared to recent DL models.