Recent improvements in deepfake creation have made deepfake videos more realistic. Moreover, open-source software has made deepfake creation more accessible, which reduces the barrier to entry for deepfake creation. This could pose a threat to the people’s privacy. There is a potential danger if the deepfake creation techniques are used by people with an ulterior motive to produce deepfake videos of world leaders to disrupt the order of countries and the world. Therefore, research into the automatic detection of deepfaked media is essential for public security. In this work, we propose a deepfake detection method using upper body language analysis. Specifically, a many-to-one LSTM network was designed and trained as a classification model for deepfake detection. Different models were trained by varying the hyperparameters to build a final model with benchmark accuracy. We achieved 94.39% accuracy on the deepfake test set. The experimental results showed that upper body language can effectively detect deepfakes.

Cisplatin (CDDP) is a platinum-based drug effective against various cancers, including lung, bladder, prostate, ovarian, esophageal, stomach, and cervical cancer and malignant lymphoma. It plays a central role as the first choice in current anticancer therapy. However, CDDP causes serious side effects, such as neurological disorders, myelosuppression, and renal damage, because it is a small molecule indiscriminately distributed in normal tissues. Therefore, it is very important to prevent or attenuate CDDP toxicity. We hypothesized that liposomalization of CDDP could alleviate serious side effects. Therefore, this study aimed to evaluate the safety and efficacy of CDDP liposomes. A patient with multiple recurrent liver metastases from metastatic nasal carcinoma was administered CDDP liposomes with consent. Magnetic resonance imaging showed that the patient remained stable-diseased; however, no apparent side effects were observed, and blood draw data showed no worsening of renal function. Patients undergoing partial pancreatectomy and jejunoileal biliary anastomosis for biliary tract cancer who consented to receive CDDP liposomes demonstrated a partial response on angiographic computed tomography; however, they showed slight fatigue. To our knowledge, the present study is the first in Japan to suggest that liposomalization of CDDP may have anticancer effects while alleviating renal damage and bone marrow suppression.

This research delves into the realm of predictive modeling within artificial intelligence (AI) and introduces an innovative ensemble approach designed to enhance prognostic precision. The proposed methodology, characterized by its ebullient nature, leverages the collective wisdom of diverse predictive models, synthesizing a comprehensive and robust framework. The pursuit of parsimony are paramount, underscoring the need for efficient model architectures that balance predictive acuracy and computational efficiency. Our study meticulously explores the amalgamation of diverse algorithms, encapsulating the intrinsic complexity of real-world phenomena. We navigate the intricate landscape of feature selection, algorithmic diversity, and model combination, fostering a holistic understanding of the prognostic process. The ebullient ensemble emerges as a dynamic solution, harnessing the synergy of constituent models while mitigating the risk of overfitting. To validate the efficacy of our approach, extensive experiments are conducted on diverse datasets, spanning domains such as healthcare, finance, and natural language processing. Comparative analyses demonstrate the superior predictive performance of our ensemble model in comparison to individual algorithms and traditional ensemble methods. Additionally, we delve into interpretability aspects, elucidating the underlying mechanisms that contribute to the model's prognostic prowess. Our research advances the frontier of predictive modeling in AI, offering a nuanced and sophisticated paradigm for prognostication. The ebullient ensemble approach stands as a testament to the synergy achievable through the amalgamation of diverse predictive models, providing a pragmatic and parsimonious avenue for enhanced prognostic acuracy in artificial intelligence applications.

Deep learning (DL) algorithms have recently emerged from machine learning and soft computing techniques. Since then, several deep learning (DL) algorithms have been recently introduced to scientific communities and are applied in various application domains. Today the usage of DL has become essential due to their intelligence, efficient learning, accuracy and robustness in model building. However, in the scientific literature, a comprehensive list of DL algorithms has not been introduced yet. This paper provides a list of the most popular DL algorithms, along with their applications domains.

The sustainability of the planet and its inhabitants is in dire danger and is among the highest priorities on global agendas such as the Sustainable Development Goals (SDGs) of the United Nations (UN). Solar energy -- among other clean, renewable, and sustainable energies -- is seen as essential for environmental, social, and economic sustainability. Predicting solar energy accurately is critical to increasing reliability and stability, and reducing the risks and costs of the energy systems and markets. Researchers have come a long way in developing cutting-edge solar energy forecasting methods. However, these methods are far from optimal in terms of their accuracies, generalizability, benchmarking, and other requirements. Particularly, no single method performs well across all climates and weathers due to the large variations in meteorological data. This paper proposes SENERGY (an acronym for Sustainable Energy), a novel deep learning-based auto-selective approach and tool that, instead of generalising a specific model for all climates, predicts the best performing deep learning model for GHI forecasting in terms of forecasting error. The approach is based on carefully devised deep learning methods and feature sets through an extensive analysis of deep learning forecasting and classification methods using ten meteorological datasets from three continents. We analyse the tool in great detail through a range of metrics and methods for performance analysis, visualization, and comparison of solar forecasting methods. SENERGY outperforms existing methods in all performance metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Forecast Skills (FS), Relative Forecasting Error, and the normalised versions of these metrics. The proposed auto-selective approach can be extended to other research problems such as wind energy forecasting and predict forecasting models based on different criteria (in addition to the minimum forecasting error used in this paper) such as the energy required or speed of model execution, different input features, different optimisations of the same models, or other user preferences.

This paper provides a review of an emerging field in the food processing sector, referring to efficient and safe food supply chains, ’from farm to fork’, as enabled by Artificial Intelligence (AI). Recent advances in machine and deep learning are used for effective food production, energy management and food labeling. Appropriate deep neural architectures are adopted and used for this purpose, including Fully Convolutional Networks, Long Short-Term Memories and Recurrent Neural Networks, Auto-Encoders and Attention mechanisms, Latent Variable extraction and clustering, as well as Domain Adaptation. Three experimental studies are presented, illustrating the ability of these AI methodologies to produce state-of-the-art performance in the whole food supply chain. In particular, these concern: (i) predicting plant growth and tomato yield in greenhouses, thus matching food production to market needs and reducing food waste or food unavailability; (ii) optimizing energy consumption across large networks of food retail refrigeration systems, through optimal selection of systems that can get shut-down and through prediction of the respective food de-freezing times, during peaks of power demand load; (iii) optical recognition and verification of food consumption expiry date in automatic inspection of retail packaged food, thus ensuring safety of food and people’s health.