International Journal on Artificial Intelligence Tools

Recently, social media platforms have been widely utilized as information sources due to their effortless accessibility and reduced costs. However, online platforms like Instagram, Twitter and Facebook get influenced by their users via fake news/reviews. The main intention of spreading fake news is to mislead other network users, which highly affects businesses, political parties, etc. Thus, an effective methodology is needed to predict fake news from social media automatically. The major objective of this proposed study is to identify and classify the given Twitter input data as real or fake through deep learning mechanisms. The proposed study involves four stages: pre-processing, embedded word analysis, feature extraction, and fake news/reviews prediction. Initially, pre-processing is performed to enhance the quality of data with the help of tokenization, stemming and stop word removal. Embedded word analysis is done using Advanced Word2Vec and GloVe modeling to enhance the performance of a proposed prediction model. Then, the hybrid deep learning model named Dense Convolutional assisted Gannet Optimal Bi-directional Network (DC_GO_BiNet) is introduced for feature extraction and prediction. A Dense Convolutional Neural Network (DCNN) is hybridized with a bi-directional long-short-term memory (Bi-LSTM) model to extract the essential features and predict fake news from the given input text. Also, the proposed model’s parameters are fine-tuned by adopting a gannet optimization (GO) algorithm. The proposed study used three different datasets and obtained higher classification accuracy as 99.5% in Fake News Detection on Twitter EDA, 99.59% in FakeNewsNet and 99.51% in ISOT. The analysis proves that the proposed model attains higher prediction results for each dataset than others.

In order to model nonlinear dynamic systems quickly and accurately, a recurrent stochastic configuration network (RSCN) with universal approximation ability is proposed in this paper. The method consists of network parameter learning and network structure determination. The network parameter learning strategy consists of input weights and the biases of hidden nodes generated randomly through a supervision mechanism, while the output weights of hidden nodes are determined by solving the least-squares problem. The network structure is determined incrementally by the training error of a single-layer recurrent neural network. The effectiveness of RSCN is tested and evaluated by using three nonlinear dynamic functions and historical data from a municipal solid waste incineration (MSWI) process. The experimental results show that the proposed method can improve modeling accuracy and reduce training time, which is valuable for applications in the field of industrial process modeling.

Visual question answering (VQA) research has garnered increasing attention in recent years. It is considered a visual Turing test because it requires a computer to respond to textual questions based on an image. Expertise in computer vision, natural language processing, knowledge understanding, and reasoning is required to solve the problem of VQA. Most techniques employed for VQA consist of models that are developed to learn the combination of image and question features along with the expected answer. The techniques chosen for image and question feature extraction and combining the features change with each model. This method of teaching a model of the question–answer pattern is ineffective for queries that involve counting and reasoning. This approach also requires considerable resources and large datasets for the training. The general VQA datasets feature a restricted number of items as responses to counting questions ($<10$), and the distribution of the answers is not uniform. To investigate these issues in VQA, we created synthetic datasets that could be modified to adjust the number of objects in the image and the amount of occlusion. Specifically, a zero-shot learning VQA system was devised for counting-related questions that provide answers by analyzing the output of an object detector and the query keywords. Using synthetic datasets, our model generated 100% correct results. Testing on the benchmark datasets task directed image understanding challenge (TDIUC) and TallyQA-simple indicated that the proposed model matched the performance of the learning-based baseline models. This methodology can be used efficiently for counting VQA questions confined to certain domains when the number of items to be counted is significant.

In today’s information age, intelligent manufacturing has become a significant trend of future industrial development. The neural network plays a vital role as the basis of smart manufacturing. This is a kind of intelligent algorithm that can simulate human brain organization, with strong learning ability and computing power, and has achieved remarkable results in the application field; the most typical representative is deep neural network, especially in recent years, an artificial intelligence developed based on deep neural network has entered the application stage, providing a new idea and model for industrial production. Based on this, taking the clothing production process as an example, this paper combined transfer learning and network fine-tuning technology to build a Faster RCNN model based on the ResNet-101 network to realize the positioning and recognition of clothing style map features and create an intelligent generation platform for clothing process flow on this basis.

Constraint Programming (CP) is a successful methodology for solving combinatorial problems from various domains. Efficiently modeling the problem at hand as a Constraint Satisfaction Problem is a crucial, but difficult task in CP. Toward this, a recent approach that is attracting increasing interest is Constraint Acquisition, i.e., the (semi)automatic learning of constraints through examples of solutions and non-solutions. This paper introduces a hybrid methodology that combines passive and active learning strategies to acquire both global and fixed arity constraints. This hybrid approach leverages the strengths of both techniques to address their individual limitations. Passive learning rapidly learns constraints from example solutions, while active learning refines and contextualizes constraints through user interaction. The core of the methodology consists of a passive learning module where subsets of variables are compared against global constraints and are validated using a CP solver. Constraints consistently present across multiple solution sets are identified as global constraints that belong to the model. Then, fixed arity constraints are refined through an active learning module with user input. Experiments across various problem types, from simple to complex, demonstrate the efficiency of the proposed hybrid methodology.

There are certain difficulties and unpleasant issues related to conventional diagnostic tools. These factors tilted the researchers toward finding an alternative non-invasive way of diagnosis. This alternate approach usually involves physiological and lifestyle-related data. The non-invasive tools are more convenient for common people as they are user-friendly and have no side effects. At the same time, they are cost-effective as well. The non-invasive diagnosis is also preferred by the people who live in places where medical facilities are not abundant. This study concentrates on detecting a person as hypertensive by analyzing certain parameters in speech using machine learning approaches. We identify some phonemes and features of speech that are more sensitive to capture the distortions in speech due to hypertension. Four different machine learning methods involving both classical and state-of-the-art methods in our study show the effectiveness of both types of machine learning methods in different dimensions. The study shows inspiring results in terms of prediction accuracy ($\sim$95%) as well as identifying a minimal set of hypertension-sensitive features. It is also found that when we combine the predictions of both classical and state-of-the-art methods, the result gives more reliable predictions.

Science automation, driven by advanced technologies, such as artificial intelligence (AI), is reshaping research in a variety of disciplines. The exponential growth of publications increases the need for automation in systematic literature reviews (SLRs). In this study, we investigated how AI applications support SLRs at different stages. After evaluating twenty-two AI applications, we identified the most useful based on their functionalities in the SLR process. This study provides important insights into the dynamic interplay between science automation, AI, and a systematic literature review. The main contribution is the identification and discussion of the relevance of the analyzed applications at different stages of the systematic literature review. We also discuss the potential for the automation and augmentation of research activities to address various scientific problems.