Numerous enhancements have been made to the mobile internet, which leads to an increase the people’s attention to posting more multi-modal posts among the social media platforms. Hence, this paper aims to design a multimodal fake news detection model with enhanced deep learning architecture. Initially, the multi-modal information including images and text are gathered from real-time social media. Then, pre-processing of both images and text is carried out. Further, the text features are extracted using Word2vector and glove embedding techniques. Then, the optimal features from both image features and text features are attained using the Adaptive Water Strider Algorithm (A-WSA). The achieved optimal features are forwarded to the new feature fusion concept based on the weight factor that is optimized using the same A-WSA. Finally, the fused features are forwarded to the fake news classification stage with the help of “Optimized-Bidirectional Long Short-Term Memory (O-BiLSTM),” where the hyperparameter of BiLSTM is optimized through similar A-WSA. Throughout the result analysis, the accuracy rate of the designed A-WSA-BiLSTM method is attained at 96.51%. The experimental result demonstrates that the proposed model is effective in using multi-modal data in automated fake news classification.

ABSTRACT Optimizing routes in road networks is crucial for smooth transportation and economic progress. Different methods exist for finding the best routes, including genetic algorithms, particle swarm optimization, and simulated annealing. Ant Colony Optimization (ACO) stands out for its efficiency. In this study, we introduce a modified version called MACO, which considers accidents when determining optimal routes. Evaluating different ACO versions reveals differences in solution quality, runtime, and number of iterations. Performance metrics including maximum obtained solution, runtime, and iteration number were evaluated for each method. In Case 1, TACO, and AACO both achieved a maximum of 21 solutions from the available possible solution of 24, exhibiting run‐times of 0.4359 and 0.4575 s, respectively. Meanwhile, MACO attained a maximum of 22 solutions from available possible solution 24, in a runtime of 0.5345 s and 10 iterations. In the second scenario, TACO, AACO, and MACO achieved maximum solutions of 20 with obtained solutions of 15, 16, and 17, respectively. TACO demonstrated a runtime of 0.1853 s with 26 iterations, AACO ran in 0.1749 s with 22 iterations, and MACO completed in 0.5799 s with 15 iterations. These findings highlight the varying performance of the optimization methods and suggest MACO as a promising approach for balancing solution quality and computational efficiency in road network path optimization.

A lot of fake news is published with the help of television and social media. As a consequence, we are affected by the disinformation and misinformation spreading in our community. It is implemented to identify fake news creators to prevent spreading misinformation about the people. Thus, it is published like real news for the reputation and finances of an individual. It is challenging, because that is created by the integration of real and false details, and images are attached like originals to confuse the public. Few fake news detection tools are available to detect fake information. To solve this fake news spreading problem, an effectual multimodal fake news detection system is proposed based on the deep learning technique. In the beginning, the input image and text are gathered from benchmark data sources. Consequently, the deep features are extracted from raw images and text. The dilated Visual Geometry Group 16 (VGG16) is adopted to extract the image features and similarly, the text features are retrieved from the Dilated Text Convolutional Neural Network (DTCNN). After achieving two different features, it is upgraded into weighted fused features, in which the weight is tuned by an Adaptive Controlling Parameter-based Chameleon Swarm Algorithm (ACP-CSA). Finally, the fused features are fed as given to the Dilated Adaptive Deep Temporal Convolution Network with Bi-directional Long Short-Term Memory (DADTCN-Bi-LSTM) for predicting the fake news. The analysis is further performed by tuning the parameters in the model using developed ACP-CSA. The efficiency of the model is investigated and results are conducted. Thus, the analysis of the suggested system shows 95 regarding accuracy, sensitivity, and specificity. The analysis of the F1-score achieves the value of 90 in the developed model. Hence, the findings demonstrate that it achieves a better detection process to evade the existence of misinformation.

Software defect severity level helps to indicate the impact of bugs on the execution of the software and how rapidly these bugs need to be addressed by the team.The working team is regularly analyzing the bugs report and prioritizing the defects.The manual prioritization of these defects based on the experience may be an inaccurate prediction of the severity that will delay in fixing of critical bugs.It is compulsory to automate the process of assigning an appropriate level of severity based on bug report results with an objective to fix critical bugs without any delay.This work aims to develop defect severity level prediction models that have the ability to assign severity level of defects based on bugs report.In this work, seven different word embedding techniques are applied to defect description to represent the word, not just as a number but as a vector in n-dimensional space in order to reduce the number of features.Since the predictive ability of the developed models depends on the vectors extracted from text as they are used as an input to the defect severity level prediction models.Further, three feature selection techniques have been applied to find the right set of relevant vectors.The effectiveness of these word embedding techniques and different sets of vectors are evaluated using eleven different classification techniques with Synthetic Minority Oversampling Technique (SMOTE) to overcome the class imbalance problem.The experimental results show that the word embedding, feature selection techniques and SMOTE have the ability to predict the severity level of the defect in a software.