A Software-Defined Network (SDN) was designed to simplify network management by allowing the control and management of the entire network from a single place. SDN is commonly used in today’s data center network infrastructures, but new forms of threats such as Distributed Denial-of-Service (DDoS), web attack, and the U2R (User to Root) attack are significant issues that might restrict the widespread adoption of SDNs. Intruders are attractive to SDN controllers because they are valuable targets. An SDN controller can be hijacked by an attacker and used to route traffic in accordance with its own needs, resulting in catastrophic consequences for the whole network. While the unified vision of SDN and deep learning methods opens new possibilities for the security of IDS deployment, the effectiveness of the detection models is dependent on the quality of the training datasets. Even though deep learning for NIDSs has lately shown promising results for a number of issues, the majority of the studies overlooked the impact of data redundancy and an unbalanced dataset. As a consequence, this may adversely affect the resilience of the anomaly detection system, resulting in suboptimal model performance. In this study, we created a hybrid Convolutional Neural Network (CNN) and bidirectional long short-term memory (BiLSTM) network to enhance network intrusion detection using binary and multiclass classification. The effectiveness of the proposed model was tested and assessed using the most frequently used datasets (UNSW-NB15 and NSL-KDD). In addition, we used the InSDN dataset, which is specifically dedicated to SDN. The outcomes demonstrate the efficiency of the proposed model in achieving high accuracy and requiring less training time.

Being one of the most widely used social media tools, Twitter is seen as an important source of information for acquiring people’s attitudes, emotions, views and feedbacks. Within this context, Twitter sentiment analysis techniques were developed to decide whether textual tweets express a positive or negative opinion. In contrast to lower classification performance of traditional algorithms, deep learning models, including Convolution Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM), have achieved a significant result in sentiment analysis. Although CNN can extract high-level local features efficiently by using convolutional layer and max-pooling layer, it cannot effectively learn sequence of correlations. On the other hand, Bi-LSTM uses two LSTM directions to improve the contexts available to deep learning algorithms, but Bi-LSTM cannot extract local features in a parallel way. Therefore, applying a single CNN or single Bi-LSTM for sentiment analysis cannot achieve the optimal classification result. An integrating structure of CNN and Bi-LSTM model is proposed in this study. ConvBiLSTM is implemented; a word embedding model which converts tweets into numerical values, CNN layer receives feature embedding as input and produces smaller dimension of features, and the Bi-LSTM model takes the input from the CNN layer and produces classification result. Word2Vec and GloVe were distinctly applied to observe the impact of the word embedding result on the proposed model. ConvBiLSTM was applied with retrieved Tweets and SST-2 datasets. ConvBiLSTM model with Word2Vec on retrieved Tweets dataset outperformed the other models with 91.13% accuracy.

The advancements in deep learning (DL) and transfer learning (TL) have transformed artificial intelligence, particularly in image classification. This research examines the theoretical foundations of DL and TL, focusing on their applications in medical image classification, specifically distinguishing between COVID-19, viral pneumonia, and normal lung conditions. By leveraging GPU-enabled high-performance computing and large labeled datasets, DL models particularly Convolutional Neural Networks (CNNs) such as ResNet50 and VGG16 have achieved superior accuracy compared to traditional machine learning methods. This study explores feature extraction using pre-trained models, the implementation of classifiers like Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), and the integration of multi-view learning techniques, such as early fusion. The results demonstrate the effectiveness of DL and TL in improving classification performance, highlighting their significant potential to advance global healthcare diagnostics.

This study examined user preferences for ChatGPT-powered conversational interfaces vs traditionaltechniques. The study collected data from 175 selected volunteers utilizing a survey questionnaire.Descriptive and inferential statistics were used to detect user preferences and compare them to theliterature review. The study found that 70% of users chose ChatGPT-powered conversational interfacesover traditional techniques, citing convenience, efficiency, and personalization. Demographic data wasexplored. The participants were evenly distributed between male and female (50%) and aged 18 to 55(mean = 35 years). This study affects ChatGPT and conversational AI development. The results indicatethat users want to use these technologies in their daily lives. To improve ChatGPT, further study is neededin this area. However, this study's tiny sample size must be considered. To confirm these findings andinvestigate other factors affecting conversational interface user preferences, bigger and more diversesamples are needed.

Deep learning intrusion detection systems perform well when traffic is merely separated into normal and malicious, but fine-grained recognition of the specific attack family remains difficult in Internet of Things (IoT) environments because of severe class imbalance and overlapping feature distributions. This work proposes an attention-enhanced CNN-BiLSTM fusion model for the multi-class classification of IoT attacks over eight families. A convolutional branch extracts local feature interactions, whereas a bidirectional Long Short-Term Memory (BiLSTM) branch reads the standardized flow descriptor as an ordered sequence and encodes dependencies among non-adjacent feature segments in both directions. Multi-head self-attention and a bidirectional cross-attention block let the two representations interact dynamically and suppress redundant information. Class imbalance is addressed with a focal loss and class-balanced weighting. The framework was evaluated on the large-scale CIC-IoT2023 benchmark under an eight-class taxonomy. On more than 3.5 × 10⁵ test flows, the proposed model reached 99.06% accuracy and a 98.99% weighted F1-score, outperforming standalone CNN, LSTM, CNN-LSTM, and CNN-BiLSTM baselines retrained on the same corrected data pipeline under an identical objective and budget.