Recently, the emerging technologies have been constantly shaping the education domain, especially the use of artificial intelligence (AI) for language learning, which has attracted significant attention. Many of the AI tools are being used for learning foreign languages, in both formal and informal ways. There are many studies that have explored the potential of the recent technology “ChatGPT” for education and learning languages, but none of the existing studies have conducted any exploratory study for assessing the usability of ChatGPT. This paper conducts an assessment for usability of ChatGPT for formal English language learning. The study uses a standard questionnaire-based approach to ask participants about their feedback for usefulness and effectiveness of ChatGPT. The participants were asked for their feedback after performing series of tasks related to formal English language learning with ChatGPT. A variety of student participants were selected for this study with diverse English language proficiency levels, education levels, and nationalities. The quantitative analysis of the participant responses shed light on their experience with regards to the usability of ChatGPT for performing different English language learning tasks such as conversation, writing, grammar, and vocabulary. The findings from this study are quite promising and indicate that ChatGPT is an effective tool to be used for formal English language learning. Overall, this study contributes to the fast-growing research domain on using emerging technologies for formal English language learning by conducting in-depth assessment of usability for ChatGPT in formal English language learning.

The widespread propagation of misinformation on social media platforms poses a significant concern, prompting substantial endeavors within the research community to develop robust detection solutions. Individuals often place unwavering trust in social networks, often without discerning the origins and authenticity of the information disseminated through these platforms. Hence, the identification of media-rich fake news necessitates an approach that adeptly leverages multimedia elements and effectively enhances detection accuracy. The ever-changing nature of cyberspace highlights the need for measures that may effectively resist the spread of media-rich fake news while protecting the integrity of information systems. This study introduces a robust approach for fake news detection, utilizing three publicly available datasets: WELFake, FakeNewsNet, and FakeNewsPrediction. We integrated FastText word embeddings with various Machine Learning and Deep Learning methods, further refining these algorithms with regularization and hyperparameter optimization to mitigate overfitting and promote model generalization. Notably, a hybrid model combining Convolutional Neural Networks and Long Short-Term Memory, enriched with FastText embeddings, surpassed other techniques in classification performance across all datasets, registering accuracy and F1-scores of 0.99, 0.97, and 0.99, respectively. Additionally, we utilized state-of-the-art transformer-based models such as BERT, XLNet, and RoBERTa, enhancing them through hyperparameter adjustments. These transformer models, surpassing traditional RNN-based frameworks, excel in managing syntactic nuances, thus aiding in semantic interpretation. In the concluding phase, explainable AI modeling was employed using Local Interpretable Model-Agnostic Explanations, and Latent Dirichlet Allocation to gain deeper insights into the model’s decision-making process.

Melanoma is one of the most lethal forms of skin cancer. It occurs on the skin surface and develops from cells known as melanocytes. The same cells are also responsible for benign lesions commonly known as moles, which are visually similar to melanoma in its early stage. If melanoma is treated correctly, it is very often curable. Currently, much research is concentrated on the automated recognition of melanomas. In this paper, we propose an automated melanoma recognition system, which is based on deep learning method combined with so called hand-crafted RSurf features and Local Binary Patterns. The experimental evaluation on a large publicly available dataset demonstrates high classification accuracy, sensitivity, and specificity of our proposed approach when it is compared with other classifiers on the same dataset.

At present, emerging technologies, such as machine learning, deep learning, or various forms of artificial intelligence are penetrating different fields of education, including foreign language education (FLE). Moreover, the current young generation was born into the technological environment, and they perceive technologies as being an indispensable part of their everyday life. However, they mainly use technologies in their informal learning, but there is not much research into emerging technologies in FLE, namely in teaching and learning English as an applied language. Therefore, the purpose of this systematic review is to identify, bring together, compare and analyze all of the technologies that are currently efficiently employed in foreign language teaching and learning, and based on the findings of the detected experimental studies, we provide specific pedagogical implications on how to use these technologies in the acquisition of English as an applied language at the university level. The methodology followed the PRISMA guidelines for systematic reviews and meta-analyses. The results of the detected experimental studies revealed that there was a serious lack of the latest technologies, such as chatbots or virtual reality (VR) devices, that are being empirically employed in a foreign language (FL) education. Moreover, mobile apps are merely focused on the development of FL vocabulary. The findings also indicate that although the FL teachers might theoretically know about these latest technological devices, such as neural machine translation, they do not know how to practically implement them in their teaching process. Therefore, this research suggests that teachers must be trained and pedagogically guided on how to purposefully implement them in their FL classes to support traditional instruction in order to identify what skills or language structures could be developed through their use. In addition, it is also claimed that more experimental studies are needed to clearly the evidence and its usefulness in teaching a foreign language as an applied language.

In recent years, online shopping has surged in popularity, with customer reviews becoming a crucial aspect of the decision-making process. Reviews not only help potential customers make informed choices, but also provide businesses with valuable feedback and build trust. In this study, we conducted a thorough analysis of the Amazon reviews dataset, which includes several product categories. Our primary objective was to accurately classify sentiments using natural language processing, machine learning, ensemble learning, and deep learning techniques. Our research workflow encompassed several crucial steps. We explore data collection procedures; preprocessing steps, including normalization and tokenization; and feature extraction, utilizing the Bag-of-Words and TF–IDF methods. We conducted experiments employing a variety of machine learning algorithms, including Multinomial Naive Bayes, Random Forest, Decision Tree, and Logistic Regression. Additionally, we harnessed Bagging as an ensemble learning technique. Furthermore, we explored deep learning-based algorithms, such as CNNs, Bidirectional LSTM, and transformer-based models, like XLNet and BERT. Our comprehensive evaluations, utilizing metrics such as accuracy, precision, recall, and F1 score, revealed that the BERT algorithm outperformed others, achieving an impressive accuracy rate of 89%. This research provides valuable insights into the sentiment analysis of Amazon reviews, aiding both consumers and businesses in making informed decisions and enhancing product and service quality.

The emergence and evolution of Internet of Things (IoT) offers great advantages to improve substantially the management over electricity consumption and distribution to the benefit of consumers, suppliers and grid operators. However, introducing IoT related devices and technologies in smart grids might lead to new security and privacy challenges. Though necessary technological innovations to ensure secure communication are being developed, more work is still required towards more secure standards for communication between devices and Smart Grids. This paper provides an overview about the security and privacy challenges of IoT applications in smart grids. Furthermore, we highlight and analyze some solutions and practices being used to cope with security and privacy requirements for IoT on deployment and management of smart grid. We address three types of challenge domains: customer domain, information and communication domain, and the grid domain.